Dynamically reconfigurable insurance product

ABSTRACT

A dynamically reconfigurable insurance product, system, and computer-implemented method may, with customer permission or consent, collect customer data; analyze the customer data to determine life events, and customer location and activities; and dynamically adjust the insurance product in real-time or substantially real-time. The dynamically reconfigurable insurance product may include several types of insurance, such as auto, home, life, personal articles, etc. From the data collected, risk levels associated with the insured, their family, and/or personal belongings may be adjusted. Based upon the risk levels determined, different types of the insurance within the insurance product may be updated, or new types of insurance may be added to the insurance product. For instance, based upon a marriage or birth of a child, life insurance coverage may be added or increased. The customer may then be notified of the changes, or proposed changes, and approve or reject the changes to the insurance product.

RELATED APPLICATIONS

The current patent application is a non-provisional patent applicationwhich claims priority benefit to the following identically-titled U.S.Provisional Applications: Ser. No. 62/175076, filed Jun. 12, 2015; Ser.No. 62/218256, filed Sep. 14, 2015; Ser. No. 62/237875, filed Oct. 6,2015; Ser. No. 62/270099, filed Dec. 21, 2015; Ser. No. 62/292497, filedFeb. 8, 2016; and Ser. No. 62/307221, filed Mar. 11, 2016. The listedearlier-filed provisional applications are hereby incorporated byreference in their entireties into the current patent application.

FIELD OF THE INVENTION

The present embodiments relate generally to insurance. Moreparticularly, the present embodiments relate to performing certainactions, and/or adjusting insurance policies, or dynamic insurance orfinancial products, based upon (i) customer-related data indicative ofrisk, or lack thereof; (ii) changing insurance or financial needs;and/or (iii) life events, customer activity, or life circumstances.

BACKGROUND

Conventional insurance techniques and policies may not provide adequateinsurance coverage to insurance customers. Insurance policies may bebased upon inadequate or aged customer information. Additionally,customers may not have the time or interest in reviewing insuranceoptions and then selecting an appropriate insurance policy. Conventionalinsurance techniques may also suffer from the lack of incentivizing thepreferred types of risk averse behaviors; failure to properly and timelyidentify risks associated with an individual and/or their insuredproperties; inefficient or ineffective customer communications; and/orother drawbacks.

The present embodiments may overcome these and/or other deficiencies.

BRIEF SUMMARY

The present embodiments may disclose systems and methods that mayprovide a dynamically reconfigurable insurance product. The dynamicallyreconfigurable insurance product may include several types of insurance,such as auto, homeowners, life, renters, personal articles, burial, pet,and/or other types of insurance, and/or may include several differentcoverages, deductibles, and/or limits. A signal premium, rate, ordiscount for the dynamic insurance product may be periodically chargedto the customer, and/or the cost of additional coverages may bedynamically charged to the customer, or the savings on less coverage maybe dynamically refunded or otherwise returned or credited to thecustomer.

Further, the present embodiments may relate to the intersection ofinsurance and data collection via electronic devices. Customer-relatedinformation and /or other data, such as GPS (Global Positioning System)data, may be gathered with customer permission via one or more sources,including mobile devices (e.g., smart phones, smart glasses, smartwatches, smart wearable devices, smart contact lenses, and/or otherdevices capable of wireless communication or data transmission); smartvehicles; smart vehicle or smart home mounted sensors; third partysensors or sources of data (e.g., other vehicles, public transportationsystems, government entities, and/or the internet); social media orsocial websites; and/or other sources of information. In some aspects,the customer data may include customer or vehicle location data, and/oreven conventional telematics data. With customer consent, the customerdata may be analyzed to determine that a life event has happened or isabout to happen; that a risk level for the customer or their propertieshas changed; and/or that customer needs have changed. Based uponcomputer analysis of the customer data, the dynamically reconfigurableinsurance product may be adjusted, such as to provide more appropriateinsurance coverage to the customer.

In one aspect, a computer-implemented method of providing and adjustinga dynamically reconfigurable insurance product covering multiple typesof insurance to an insured may be provided. The method may include, withcustomer permission, (1) receiving, at or via one or more processors(such as a remote server or processor associated with an insuranceprovider), customer-related data; (2) determining, at or via the one ormore processors, a life event (or other customer activity), and/or typethereof, from computer analysis of the customer-related data; (3)adjusting, at or via the one or more processors, the dynamicallyreconfigurable insurance product (and/or an associated dynamic insuranceproduct premium or discount) based upon, at least in part, the computeranalysis of the customer-related data and/or life event (or othercustomer activity), and/or type thereof, determined; (4) generating, ator via the one or more processors, a notification of the adjustment (orrecommended adjustment) to the dynamically reconfigurable insuranceproduct, such as a wireless communication or data transmissionnotification; (5) transmitting, via the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), the notification to a mobile device or other computingdevice of the insured for the insured's review, approval, and/orrejection; (6) receiving, via or at the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), an approval or rejection of the adjustment (orrecommended adjustment) to the dynamically reconfigurable insuranceproduct from the mobile device or other computing device of the insured;and/or (7) adjusting or updating an insurance premium and/or discountassociated with the dynamically reconfigurable insurance product, at orvia the insurance provider remote server, to facilitate adjusting orotherwise providing a dynamic insurance product to the insured thatreflects current or changing insurance needs of the customer.

The customer data collected with the customer's consent may facilitate(i) providing more adequate or appropriate insurance coverages and/ortypes of insurance to the customer in a timely manner; (ii) providingmore accurate behavior and/or usage-based insurance; (iii) incentivizingless risky behavior; (iv) providing recommendations that lower risk ormeet changing insurance needs of the customer, such as by recommendingdifferent or new types of insurance and/or different insurance coverageor deductible amounts or levels, and/or other types of recommendations.The present embodiments may reward an insured for exhibiting risk-averseor low risk behavior in the form of lower insurance premiums or ratesthat may be dynamically adjusted, and/or may provide dynamic insurancediscounts, points, and/or rewards. In one embodiment, the customer mayopt-in to a rewards or insurance discount program.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of devices andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals. The present embodiments are notlimited to the precise arrangements and instrumentalities shown in theFigures.

FIG. 1 illustrates various components of an exemplary system foradjusting a dynamically reconfigurable insurance product;

FIG. 2 illustrates various components of an exemplary server that may beused with the system and shown in block schematic form;

FIG. 3 illustrates various components of an exemplary mobile electronicdevice that may be used with the system and shown in block schematicform;

FIG. 4 illustrates an exemplary dynamically reconfigurable insuranceproduct that changes over time based upon computer analysis ofcustomer-related data;

FIG. 5 illustrates an exemplary computer-implemented method of adjustinga dynamically reconfigurable insurance product based uponcustomer-related data, and/or life event (and/or changing insuranceneeds) detection;

FIG. 6 illustrates an exemplary computer-implemented method ofdetermining a type of insurance to adjust within a dynamicallyreconfigurable insurance product having several types of insurance basedupon customer-related data, and/or life event (and/or changing insuranceneeds) detection;

FIG. 7 illustrates an exemplary computer-implemented method ofdetermining a new type of insurance to add to a dynamicallyreconfigurable insurance product having several types of insurance basedupon customer-related data, and/or life event (and/or changing insuranceneeds) detection;

FIG. 8 illustrates an exemplary computer-implemented method of adjustinga dynamically reconfigurable insurance product based upon customerlocation or GPS data, and determining likely activity and/or changinginsurance needs from the customer location or GPS data (and/or lifeevent detection);

FIG. 9 illustrates an exemplary computer-implemented method ofreconfiguring a reconfigurable insurance product based upon customerengagement and/or analytics data; and

FIG. 10 illustrates an exemplary computer-implemented method ofadjusting a computer-defined dynamic product based upon customerengagement and/or customer-related data, and/or life event (and/orchanging insurance needs) detection.

The Figures depict exemplary embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the devices, applications,and methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, with customerpermission or affirmative consent, generating and/or collecting customeror customer-related data; analyzing the customer or customer-relateddata; and then dynamically adjusting an insurance product based upon thecustomer or customer-related data and/or analysis thereof. The datagenerated and/or collected may be analyzed by an insurance providerserver or processor to provide insurance-related benefits to an insured,and/or apply the insurance-related benefits to the dynamic insurancepolicy or premium.

With the present embodiments, an insurance customer may be provided witha comprehensive insurance suite that meets their unique needs over theirlifetime. Conversely, conventional product(s) may constrain personalizedservice with the customer, such as due to a one-size-fits-all mentality;having different product types; different customers having differentneeds from the engagement experience; different customer needs existingbased upon life stage, product relevance, available time, and/or levelof knowledge; the level of accuracy and the amount of data available atany given time; and/or comfort level (for the customer, or for theinsurer), which may necessitate the involvement of an agent.

As a result, customers may be overwhelmed by the number of products inthe marketplace, and/or the complexity of those products. Also, eachcustomer is unique. Each customer may have unique needs, and may nothave the time, confidence, adequate understanding of their needs, and/orsufficient understanding of the available products and options. There isa burden on customers to identify risks and locate policies to addressthose risks, as well as identify risks based upon their individual livesand how their lives evolve over time.

Further, conventional products solutions and terms may be static,however, customer needs evolve over time. Conventional techniques mayalso rely upon agent and personal financial advisor expertise, customerrealization of their needs, and/or may be constrained by the terms ofexisting agreements.

Unlike conventional products, the present embodiments may provide a“product” experience not constrained by a list of products or bytemporal constraints. A dynamically changing insurance product may beflexible to evolve over time as the needs of the customer and/or theirfamily changes (with customer permission). The insurance product mayhave product terms that allow for this flexibility. The dynamic changesto the insurance product may include changes in price, or the pricingmay include evolution. The insurance product may include several typesof insurance products—auto, home, life, renters, personal articles, etc.

Changing customer needs may be automatically met by the dynamicinsurance product. For example, with customer consent or knowledge, morelife insurance may be automatically provided in the event of a marriageor birth of a child. The insurance product may be personalized for thecustomer, and/or include no temporal constraints, such as term length.The insurance product may not be based upon a menu of products, butrather may create a solution customized to the needs of the customer. Italso may be a self-healing product that reconfigures itself to meetchanging customer needs. The insurance product may include a singleagreement with the customer or alternatively may include several, oreven an infinite, number of (short-term or long-term) agreements.

The dynamically reconfigurable insurance product may involve a continualevaluation of the customer and/or customer (or customer-related) data(such as after receiving customer consent or opt-in to a rewards ordiscount program), and may include satisfying customer needsdynamically. As used herein, “dynamic” may mean (in addition to itsnormal, plain language, or dictionary meaning) that both the customerneeds and/or the insurance products are constantly or periodicallychanging. Embodiments of the present invention may balance these needsby human assistance and/or system intervention, however, in oneembodiment, the dynamic product and various risk profiles may beprimarily adjusted under the direction or control of a processor, suchas an insurance provider remote server, such as a processor employing orapplying machine learning techniques, object recognition or opticalcharacter recognition techniques, or other computer technologytechniques or algorithms on image data, telematics data, and other typesof data generated or collected by one or more home, mobile device, orvehicle sensors.

For instance, auto insurance may be based upon daily mileage needs andhow much an insured commutes. If one is travelling (such as to New YorkCity via plane and for which taxis may be used by the insured during thestay, and instead of driving their personal and insured vehicle), the“dynamic product” may, with customer permission or consent,automatically identify such a scenario (such as by analyzing telematicsdata, smart home data, or mobile device GPS data), and reduce autoinsurance while simultaneously adding travel insurance during the timethat the insured flies/travels.

As another example, the purchased “dynamic product” may include lifeinsurance coverage that protects a spouse and/or covers a home mortgage.Later in life when a baby is born, the “dynamic” product may, withcustomer permission or affirmative consent, automatically evolve/adaptto include the additional life insurance needs.

The dynamic product may, again with customer permission, add temporaryor permanent insurance based upon life events, customer activity orlocation, and/or other customer or customer-related data. In someinstances, a new or different type of insurance may be added. Forexample, for a young adult that is getting married or having a firstchild and is presently without life insurance, life insurance may beadded to the dynamic product and premiums adjusted accordingly. Also,based upon marriage, age, or high school or college graduation, autoinsurance premiums may be adjusted or lowered. For a family having ateenager getting a driver's license, the family's or parent's autoinsurance policy may automatically be adjusted accordingly.

In some instances, a type of insurance, and/or need therefore, may bereduced. In the event of the death of family member, the needs for autoinsurance or life insurance for the family or another individual maydecrease, and the dynamic product may be adjusted to reflect the lesserneed.

In other cases, insurance types may change. For instance, a first timehome buyer moving from an apartment may no longer need rentersinsurance, but now needs homeowners insurance. The dynamic productand/or premium may be adjusted accordingly. Other scenarios include alarge purchase or sale, such as related to a home, vehicle, boat,jewelry, or antique. In such a case, insurance covering the home,vehicle, boat, jewelry, or antique may be added or dropped dependingupon whether the item was acquired or sold.

The dynamic product may be adjusted before a conventional time periodfor insurance (such as 6 months) has expired. Thus, the dynamic productmay not be constrained by periodic payments and/or adjustments. Rather,premiums/policies may be dynamically adjusted to more accurately reflectcurrent levels of risk, or lack thereof, for the customer and/or theirbelongings.

In one scenario, the dynamic product may be adjusted based upon customerlocation (with customer permission), such as a GPS location receivedfrom their vehicle or mobile device. For instance, if a person istraveling and has left their insured house empty for a given period oftime, their home insurance and/or personal articles insurance may beadjusted to reflect an increased level of risk. On the other hand, whenthe homeowner returns home, the dynamic product may be adjusted toreflect a lower level of risk.

In one embodiment, auto insurance, renters insurance, personal articlesinsurance, and/or homeowners insurance may all be dynamically adjustedbased upon customer GPS data. For instance, a person may work and stayin the city (e.g., at an apartment) during the work week, and thenreturn to a rural home on the weekend. Their auto insurance, and/orother types of insurance, may be adjusted to reflect a higher or lowerrisk level during the week or on the weekend, depending on the situationand/or locations. For instance, either the apartment or rural home mayhave additional people living there, and/or have security (e.g.,doorman) or security systems that may impact risk levels associated withleaving the premises unoccupied for one or more days.

Also, GPS location may be monitored, such as after receiving customeraffirmative consent or permission (or opt-in to an insurance discount orrewards program), to determine when the customer is likely traveling.For instance, when the customer is about to cross a state line, they maybe on a lengthy trip—which may be monitored and/or detected by receivingand analyzing GPS data from the customer's vehicle and/or mobile device.When it is determined that the customer is likely on a lengthy trip, amessage may be transmitted to their vehicle or mobile device viawireless communication for confirmation. Once it is confirmed that theinsured is on a lengthy trip, such as receiving a confirmation from theinsured's mobile device at an insurance provider remote server viawireless communication or data transmission, the dynamic product mayadjust their auto, home, life, personal articles, travel, and/or othertypes of insurance accordingly. The GPS data may also be used to adjustdistance or mileage-based (e.g., pay-per-mile) auto insurance, and/oradjust travel insurance.

As the dynamic product is adjusted or updated, various liability orother coverages (or deductibles, limits, premiums, etc.) may be scaledup, or scaled down. After which, the customer may be promptly notified,such as via wireless communication or data transmission from aninsurance provider remote server to their mobile device. The customermay accept or reject the changes via their mobile device and wirelesscommunication back to the remote server.

The dynamic product may include layering of risk, value, and/or data ina dynamically shaping “product” to match customer expectations and/orneeds via the ebb and flow of their life. The dynamic product may bedefined and evolved by the customer needs and not by the insuranceprovider, per se. The dynamic product may be managed such that it adaptsto the individual customer needs via flexible business processes,flexible data usage, and/or the ability to evolve where necessary.

The methods and systems described herein may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware, or any combination or subset. At least oneof the technical problems addressed by this system includes: (i)inconvenient and imprecise remote comparison between prior and currentlife situation or living conditions; (ii) difficulty quantifying andcataloging any differences between dynamic life situations, livingconditions or life events; and/or (iii) an inability to provide guidanceas perceived and actual level of risk based upon an individual's currentsituation.

The technical effect achieved by this system is at least one of: (i)more convenient and efficient remote comparison between a prior lifesituation of an individual (or household) and a current life situation;(ii) simpler quantification and characterization of differences betweenprior and current dynamic life situations, living conditions or lifeevents; (iii) situation-based guidance based upon a current dynamicallychanging level of risk for an individual or household; (iv) dynamicanalysis and determination of updated risk levels or profiles for ahandful of components that together comprise the dynamicallyreconfigurable product, those components may include auto, life, home,renters, personal articles, pet, burial, other types of insurance, an/orfinancial products (checking and savings accounts, loans, etc.); and (v)training and using machine learning techniques to identify life events,and quantify changes in risk associated with each life event identifiedto update the dynamic product accordingly (such as adjust insurancecoverages, deductibles, and/or limits to more accurately reflect changesin life circumstances).

Additional technical effects, as well as insurance-related benefits,provided by the present embodiments may include, inter alia: (1) moreappropriate levels of insurance; (2) more appropriate types of insurancecoverage; (3) more timely binding of insurance based upon, at least inpart, real-time or near real-time analysis of customer orcustomer-related data, and/or customer or insured vehicle location; (4)more timely identification of risk, or lack thereof (and/or ofassociated risk level), to the insured or their belongings (home,vehicle, jewelry, etc.); (5) more accurate behavior or usage-basedinsurance and/or premiums; (6) incentivizing low risk or less riskybehavior for an insured; (7) providing recommendations that reduce riskand/or result in insurance savings for the insured; (8) identifying moreaccurate risk or risk levels in real-time or near real-time; and/orother insurance-related benefits. The present embodiments may reward aninsured for exhibiting risk-averse behavior in the form of lowerinsurance premiums or rates, or additional insurance discounts providedin real-time or approximately real-time.

Specific embodiments of the technology will now be described inconnection with the attached drawing figures. The embodiments areintended to describe aspects of the invention in sufficient detail toenable those skilled in the art to practice the invention. Otherembodiments can be utilized and changes can be made without departingfrom the scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense. Thescope of the present invention is defined only by the appended claims,along with the full scope of equivalents to which such claims areentitled.

Exemplary System

FIG. 1 depicts an exemplary environment in which embodiments of a system10 may be utilized for transmitting and receiving location informationand other customer-related data, dynamic insurance product adjustmentnotifications, and other information described herein or fairly drawntherefrom by one of ordinary skill (the “system information”). Theenvironment may include a network 12 and computer server 14 as seen inFIG. 1, with which the system 10 interfaces to send and receive systeminformation. The system 10 may broadly comprise one or more customermobile electronic devices 16, the aforementioned server 14 (which may bea remote server), databases 18, and/or a local (home) network or smarthome controller 17. The system 10 may thus be utilized to automaticallycommunicate with customers and/or their mobile electronic devices 16,insurance providers and/or their computer server(s) 14, and/or externaldatabases 18 such as those operated by social media website operators,and other sources for customer-related data, including the local or homenetwork, and/or smart home controller 17.

The network 12 (and/or the local (home) network or smart home controller17) may generally allow communication between the mobile electronicdevices 16 and the server 14, and also between the server 14 and thedatabases 18. The network 12 (and/or the local (home) network or smarthome controller 17) may include local area networks, metro areanetworks, wide area networks, cloud networks, the Internet, cellularnetworks, plain old telephone service (POTS) networks, and the like, orcombinations thereof. The network 12 (and/or the local (home) network orsmart home controller 17) may be wired, wireless, or combinationsthereof and may include components such as modems, gateways, switches,routers, hubs, access points, repeaters, towers, and the like. Themobile electronic devices 16 generally connect to the network 12 (and/orthe local (home) network or smart home controller 17) wirelessly, suchas radio frequency (RF) communication using wireless standards such ascellular 2G, 3G, or 4G, Institute of Electrical and ElectronicsEngineers (IEEE) 802.11 standards such as WiFi, IEEE 802.16 standardssuch as WiMAX, Bluetooth®, or combinations thereof.

The server 14 generally retains electronic data and may respond torequests to retrieve data as well as to store data. The server 14 may beembodied by application servers, database servers, file servers, gamingservers, mail servers, print servers, web servers, or the like, orcombinations thereof. Furthermore, the computer server 14 may include aplurality of servers, virtual servers, or combinations thereof. Thecomputer server 14 may be configured to include or execute software suchas file storage applications, database applications, email or messagingapplications, web server applications, or the like. The computer server14 may apply business methods or algorithms, may utilize lookup tablesor databases, or combinations thereof to determine insurance rates orpremiums, insurance types or scope, or similar characteristics ofinsurance coverage. Insurance providers may own one or more computerservers 14 that may, with customer permission or affirmative consent,automatically collect and analyze customer-related data and informationabout insurance policies and coverage criteria.

In one aspect, the exemplary computing technologies and system 10 shownin FIG. 1 may include a network 12, one or more vehicles 16 andrespective mobile computing devices 16 (such as smart phones, laptops,tablets, electronic wearables, and other computing devices capable of RFcommunication), and one or more external computing devices 14 ordatabases 18. In one aspect, mobile computing devices 16 may beimplemented within a vehicle or smart vehicle that may have anassociated on-board computer. Each vehicle may be configured forwireless inter-vehicle communication, such as vehicle-to-vehicle (V2V)wireless communication and/or data transmission.

Various aspects may include system 10 implementing any suitable numberof networks 12, mobile computing devices or smart vehicles 16, externalcomputing devices 14, and/or local home networks or smart homecontrollers 17. For example, system 10 may include a plurality ofexternal computing devices 14 and more than two mobile computing devices16, any suitable number of which being interconnected directly to oneanother and/or via network 12.

In one aspect, each of mobile computing devices 16 may be configured tocommunicate with one another directly via peer-to-peer (P2P) wirelesscommunication and/or data transfer. In other aspects, each of mobilecomputing devices 16 may be configured to communicate indirectly withone another and/or any suitable device via communications over network12, such as external computing device 14, for example. In still otheraspects, each of mobile computing devices 16 may be configured tocommunicate directly and indirectly with one and/or any suitable device,which may be concurrent communications or communications occurring atseparate times.

Each of mobile computing devices 16 may be configured to send data toand/or receive data from one another and/or via network 12 using one ormore suitable communication protocols, which may be the samecommunication protocols or different communication protocols as oneanother. To provide an example, mobile computing devices 16 may beconfigured to communicate with one another via a direct radio link,which may utilize, for example, a Wi-Fi direct protocol, an ad-hoccellular communication protocol, etc. Furthermore, mobile computingdevices 16 may be configured to communicate with the vehicle on-boardcomputers located in vehicles utilizing a Bluetooth® communicationprotocol (radio link not shown).

To provide additional examples, mobile computing devices 16 may beconfigured to communicate with one another via radio links 19 b, 19 c,19 d, 19 a by each communicating with network 12 or smart homecontroller/local home network 17 utilizing a cellular communicationprotocol. As an additional example, mobile computing devices 16 may beconfigured to communicate with external computing device or server 14,and/or databases 18 via radio links 19 b, 19 c, 19 d, 19 e, 19 f, and/or19 g. Still further, one or more of mobile computing devices 16 may alsobe configured to communicate with one or more smart components directlyand/or indirectly using any suitable communication protocols and radiolinks.

Mobile computing devices 16 may be configured to execute one or moremachine learning algorithms, programs, applications, etc., to determinea geographic location of each respective mobile computing device; togenerate, measure, monitor, and/or collect one or more sensor metrics,and/or types of data, such as vehicle or home telematics data; tobroadcast the data generated or collected via their respective radiolinks; to receive data or instructions via their respective radio links;to determine whether an adjustment to the dynamic product, or a risk orother component of the dynamic product should be generated, or whether alife event (or other change in living situation or circumstances) hasoccurred based upon analysis of the data generated or collected; togenerate the one or more notifications to the customer, and/or tobroadcast the notifications.

Network 12 may be implemented as any suitable network configured tofacilitate communications between mobile computing devices 16 and one ormore of external computing device 14 and/or smart home controller orlocal home network 20. For example, network 12 may include one or moretelecommunication networks, nodes, and/or links used to facilitate dataexchanges between one or more devices, and may facilitate a connectionto the Internet for devices configured to communicate with network 12.Network 12 may include any suitable number of interconnected networkcomponents that form an aggregate network system, such as dedicatedaccess lines, plain ordinary telephone lines, satellite links, cellularbase stations, a public switched telephone network (PSTN), etc., or anysuitable combination thereof. Network 12 may include, for example, aproprietary network, a secure public internet, a secure electroniccommunication network, a mobile-based network, a virtual privatenetwork, etc.

In aspects in which network 12 facilitates a connection to the Internet,data communications may take place over the network 12 via one or moresuitable Internet communication protocols. For example, network 12 maybe implemented as a wireless telephony network (e.g., GSM, CDMA, LTE,etc.), a Wi-Fi network (e.g., via one or more IEEE 802.11 Standards), aWiMAX network, a Bluetooth network, etc. Thus, radio links 19 a-19 g mayrepresent wired links, wireless links, or any suitable combinationthereof.

In aspects in which mobile computing devices 16 communicate directlywith one another in a peer-to-peer fashion, network 12 may be bypassedand thus communications between mobile computing devices 16 and externalcomputing device 16 may be unnecessary. For example, in some aspects,one mobile computing device 16 may broadcast data directly to anothermobile computing device. In this case, mobile computing device 16 mayoperate independently of network 12 to determine whether a life event ora change in living conditions has occurred and/or whether an alert ornotification should be generated at mobile computing device 16 basedupon data collected or generated via one or more sensors or a sensorarray, such as mobile device, vehicle, or home-mounted sensors. Inaccordance with such aspects, network 12 may be omitted.

However, in other aspects, one or more of mobile computing devices 16may work in conjunction with external computing device 14 to determinewhether a life event or change in living conditions has occurred and/orto generate or adjust the dynamic product. For example, in some aspects,mobile computing device 16 may broadcast data generated or collected byone or more sensors (including cameras), which is received by externalcomputing device 14. In this case, external computing device 14 may beconfigured to apply machine learning techniques on the data received anddetermine whether a life event or change in living conditions hasoccurred and/or whether a proposed adjustment to the dynamic productshould be transmitted to the mobile computing device 16.

External computing device 14 may be configured to execute variousmachine learning techniques, object recognition and optical characterrecognition techniques, software applications, algorithms, and/or othersuitable programs. External computing device 14 may be implemented asany suitable type of device to facilitate the functionality as describedherein. For example, external computing device 14 may be implemented asa network server, a web-server, a database server, one or more databasesand/or storage devices, or any suitable combination thereof. Althoughillustrated as a single device in FIG. 1, one or more portions ofexternal computing device 14 may be implemented as one or more storagedevices that are physically co-located with external computing device14, or as one or more storage devices utilizing different storagelocations as a shared database structure (e.g. cloud storage).

In some embodiments, external computing device 14 may be configured toperform any suitable portion of the processing functions remotely thathave been outsourced by one or more of mobile computing devices 16. Forexample, mobile computing device 16 may collect data (e.g., geographiclocation data, telematics data, image or audio data, other type ofsensor data, time-stamped data, other types of data units, etc.), butmay send the data (or data units) to external computing device 14 forremote processing instead of processing the data (or data units)locally. In such embodiments, external computing device 14 may receiveand process the data (or data units) to determine whether an anomalouscondition exists (such as whether a life event, or change in livingconditions, has occurred or has likely occurred or is predicted to occurshortly) and, if so, whether to adjust the dynamic product, and/ortransmit a proposed adjustment to the dynamic product to one or moremobile computing devices 16 for customer review.

In one aspect, external computing device 14 may additionally oralternatively be part of an insurer computing system (or facilitatecommunications with an insurer computer system), and as such may accessinsurer databases, execute algorithms, execute applications, accessremote servers, communicate with remote processors, etc., as needed toperform insurance-related functions. For example, external computingdevice 14 may facilitate the receipt of smart home or vehicle telematicsdata or other data from one or more mobile computing devices 16 and/orsmart home controller 17, which may be associated with insurancecustomers and/or running a telematics application that generates andbroadcasts telematics data.

In aspects in which external computing device 14 facilitatescommunications with an insurer computing system (or is part of such asystem), data received from one or more mobile computing devices 16 mayinclude logon credentials which may be verified by external computingdevice 14 or one or more other external computing devices, servers, etc.These logon credentials may be associated with an insurer profile, whichmay include, for example, insurance policy numbers, a description and/orlisting of insured assets, vehicle identification numbers of insuredvehicles, addresses of insured structures, contact information, premiumrates, discounts, etc.

In this way, data received from one or more mobile computing devices 16may allow external computing device 14 to uniquely identify each insuredcustomer and/or whether each identified insurance customer has installedthe telematics application on their mobile device 16. Furthermore, anydata collected from one or more mobile computing devices 16 may bereferenced to each insurance customer and/or any insurance policiesassociated with each insurance customer for various insurance-relatedpurposes.

In one embodiment, smart home controller 17 may be in wired or wirelesscommunication with a plurality of processor/sensor pairs located about ahome, and/or a home mounted sensor array. The sensors may generate orcollect data, and transmit (via a transceiver) the data collected to thesmart home controller 17 or a remote or external server 14 for furtheranalysis, such as detecting anomalous or changed conditions (by using,for example, machine learning techniques on the sensor data), which mayindicate a change in living situation, or a life event.

For instance, each home-mounted sensor may be in wireless RFcommunication with the smart home controller 17 or remote server 14 viaone or more radio links that utilize an IEEE communication standard.Once the data generated by one or more sensors mounted on, or locatedabout, a house is received by the smart home controller 20 or remoteserver 14, the smart home controller 17 or remote server 14 may comparethe data with a baseline of expected conditions or data units todetermine changes in the living situation within the home.

Exemplary Server And Mobile Devices

As shown in FIG. 2, the server 14 may include a communication element20, a processing element 22, a memory element 24, and a softwareapplication 26 configured to control its function according to variousembodiments described herein and otherwise within the scope of theinvention.

The communication element 20 generally allows communication withexternal systems or devices. The communication element 20 may includesignal or data transmitting and receiving circuits, such as antennas,amplifiers, filters, mixers, oscillators, digital signal processors(DSPs), and the like. The communication element 20 may establishcommunication wirelessly by utilizing RF signals and/or data that complywith communication standards such as cellular 2G, 3G, or 4G, IEEE 802.11standard such as WiFi, IEEE 802.16 standard such as WiMAX, BluetoothTM,or combinations thereof. Alternatively, or in addition, thecommunication element 20 may establish communication through connectorsor couplers that receive metal conductor wires or cables which arecompatible with networking technologies such as ethernet. In certainembodiments, the communication element 20 may also couple with opticalfiber cables. The communication element 20 may be in communication withor electronically coupled to memory element 24 and/or processing element22.

The memory element 24 may include data storage components such asread-only memory (ROM), programmable ROM, erasable programmable ROM,random-access memory (RAM) such as static RAM (SRAM) or dynamic RAM(DRAM), cache memory, hard disks, floppy disks, optical disks, flashmemory, thumb drives, USB ports, or the like, or combinations thereof.The memory element 24 may include, or may constitute, a“computer-readable medium”. The memory element 24 may store theinstructions, code, code segments, software, firmware, programs,applications, apps, services, daemons, or the like that are executed bythe processing element 22. The memory element 24 may also storesettings, data, documents, sound files, photographs, movies, images,databases, and the like.

The processing element 22 may include processors, microprocessors,microcontrollers, DSPs, field-programmable gate arrays (FPGAs), analogand/or digital application-specific integrated circuits (ASICs), or thelike, or combinations thereof. The processing element 22 may generallyexecute, process, or run instructions, code, code segments, software,firmware, programs, applications, apps, processes, services, daemons, orthe like. The processing element 22 may also include hardwarecomponents, such as finite-state machines, sequential and combinationallogic, and other electronic circuits that may perform the functionsnecessary for the operation of embodiments of the current inventiveconcept. The processing element 22 may be in communication with theother electronic components through serial or parallel links thatinclude address busses, data busses, control lines, and the like.

Software application 26 may be configured to instruct the collection ofcustomer-related data from mobile electronic devices 18 and/or fromother data sources (such as storage devices 18), such as after receivingcustomer permission. Software application 26 may therefore be configuredto instruct the performance of functions commonly associated withInternet “web crawlers” or the like and/or may be more narrowly focusedon a plurality of data sources known to house data particularly relevantto configuring a dynamically adjustable insurance product.

Software application 26 may be self-configured to determine a likelihoodthat a data source will house relevant data, for example based uponanalysis of how frequently a proposed dynamic product adjustment isaccepted or ultimately adopted when proposed based upon analysis of datafrom such a source. For another example, software application 26 mayalso be self-configured to select relevant data sources based upon aplurality of pre-programmed investigative rules. Put another way,pre-programmed rules may comprise weighted or un-weighted indicia ofrelevance, configured such that the more rules or conditions data from adata source satisfies, the greater relevance is assigned to such a datasource, leading to a self-configured preference for investigating thatdata source. A data source may be catalogued as having a certain degreeof relevance, or as being worthy of investigation or not, or mayotherwise be flagged for future action or inaction by the server 14 inconnection with its investigations, in a data source database stored inmemory element 24. Each data source may be catalogued according to IPaddress(es), URL address(es), proprietor, operator, and/or by otherinformation useful in identifying the source(s) of data or contentaccessible via the network 12.

Customer-related data collected via instruction of the softwareapplication 26 may be analyzed at or via the processing element 22according to the principles set forth herein to determine, for example,whether an event that may impact an aspect of an insurance product hasoccurred or may occur. The server 14 may further cause issuance of anotification to a customer, for example to the customer's mobileelectronic device 18, indicating an adjustment to a dynamicallyreconfigurable insurance product has been or is expected to be madeand/or requested consent for such a change.

It is envisioned that the software application 26 may be configured toinstruct the performance of additional or fewer steps of the presentinventive concept at or via the server 14.

The data storage devices 18 generally store data and each is typicallyembodied by a data server and may include storage area networks,application servers, database servers, file servers, gaming servers,mail servers, print servers, web servers, or the like, or combinationsthereof. The data storage devices 18 may be additionally oralternatively embodied by computers, such as desktop computers,workstation computers, or the like.

In addition, the data storage devices 18 may be configured to transmitand receive data to and from other devices. The data storage devices 18may have various performance specifications such as bandwidth available,jitter, latency, capacity or throughput, and the like. Furthermore, thedata storage devices 18 may have one or more currently running jobs, aswell as a queue of planned jobs for the future.

The mobile electronic devices 18 may be embodied by a smart watch, asmart phone, a personal digital assistant (PDA), a tablet, a palmtop orlaptop computer, smart glasses, or other mobile device, and is typicallycarried by the customer while driving. A mobile electronic device 18 mayalso be embodied by onboard automotive components such as a GPS receivercoupled to a wireless transmitter or other onboard telematicsdata-gathering equipment. The mobile electronic devices 18 may includelocation determining elements 28 such as GPS receivers, memory elements30, processing elements 32, software applications 34 and/orcommunications elements 36, as seen in FIG. 3. The memory elements 30may store the software applications 34, and the processing elements 32may execute the software applications 34.

The majority of components of the mobile electronic devices 18—morespecifically, the communications elements 36, processing elements 32,and memory elements 30—each operate under similar principles to thoseset forth above with respect to analogous components of the server 14.The location determining element 28 determines a current geolocation ofthe electronic device 18 and may receive and process radio frequency(RF) signals from a global navigation satellite system (GNSS) such asthe global positioning system (GPS) primarily used in the United States,the GLONASS system primarily used in the Soviet Union, or the Galileosystem primarily used in Europe. The location determining element 28 mayaccompany or include an antenna to assist in receiving the satellitesignals. The antenna may be a patch antenna, a linear antenna, or anyother type of antenna that can be used with location or navigationdevices. The location determining element 28 may include satellitenavigation receivers, processors, controllers, other computing devices,or combinations thereof, and memory. The location determining element 28may process a signal, referred to herein as a “location signal”, fromone or more satellites that includes data from which geographicinformation such as the current geolocation is derived. The currentgeolocation may include coordinates, such as the latitude and longitude,of the current location of the electronic device 18. The locationdetermining element 28 may communicate the current geolocation to theprocessing element 32.

Although embodiments of the location determining element 28 may includea satellite navigation receiver, it will be appreciated that otherlocation-determining technology may be used. For example, cellulartowers or any customized transmitting radio frequency towers can be usedinstead of satellites may be used to determine the location of theelectronic device 18 by receiving data from at least three transmittinglocations and then performing basic triangulation calculations todetermine the relative position of the device with respect to thetransmitting locations. With such a configuration, any standardgeometric triangulation algorithm can be used to determine the locationof the electronic device. The location determining element 28 may alsoinclude or be coupled with a pedometer, accelerometer, compass, or otherdead-reckoning components which allow it to determine the location ofthe electronic device 18. The location determining element 28 maydetermine the current geographic location through a communicationsnetwork, such as by using Assisted GPS (A-GPS), or from anotherelectronic device. The location determining element 28 may even receivelocation data directly from a user.

Exemplary Machine Learning

The processing element 22 may utilize machine learning programs ortechniques. For instance, the processing element 22 may utilize theinformation from the existing dynamic product and sensor data collected,and apply that data to one or more machine learning techniques togenerate a resident profile of the household. The processing element 22and/or machine learning techniques may recognize or determine patternsof activity or behavior from the sensor data. The machine learningtechniques or programs may include curve fitting, regression modelbuilders, convolutional or deep learning neural networks, or the like.The processing element 22 and/or machine learning techniques may furtherassociate activity patterns from the sensor data with individuals orpets that are known to live in the house, and/or determine when anindividual or pet has joined the household from analysis of sensor datacollected over time.

Based upon this data analysis, the processing element 22 and/or machinelearning techniques may generate the resident profile of the household.The resident profile may include the number of residents of thehousehold, the number of pets, and the like. The processing element 22and/or machine learning techniques may utilize the resident profile incombination with information regarding features of the house todetermine a level of risk for the household; and may also utilize theresident profile to determine when there is a change in the residency ofthe household, such as the adoption of a child, the birth of a baby, theaddition of a pet, the moving out of a child to an apartment or college,or the like.

As noted, data associated with the dynamic product, and/or auto, life,health, homeowners, pet or other components of the dynamic product maybe input into a machine learning program. The machine learning programmay include curve fitting, regression model builders, convolutional ordeep learning neural networks, or the like. The machine learning programmay associate activity patterns from the sensor data with individuals orpets that are known to live in the house. Each mobile device may have aunique ID, although it is not necessarily known which household membercarries each mobile device. However, this information may be inferredfrom the sensor data. For example, if it is known from the dynamicproduct information that the household includes two adults and oneteenager, then one of the mobile devices may arrive at the house in midor late afternoon every weekday. The machine learning program maydetermine that the particular mobile device belongs to the teenager andthat all of the activity that is recorded in the house before anothermobile device arrives at the house is generated from the teenager. Themachine learning program may further determine patterns of activity orbehavior that can be attributed to a first individual—the teenager, inthis case. The machine learning program may then utilize the patterns topredict future activity of the first individual. The machine learningprogram may also use the patterns to isolate the activity of the firstindividual from the activities of other individuals in the house andthus be able to determine patterns of activity of the other individuals.Furthermore, if the dynamic product information includes genderinformation regarding the residents of the house, then the machinelearning program may be able to establish gender-dependent patterns ofactivity or behavior that may be used to identify individuals or predictactivity based upon gender. Based upon this data analysis, the machinelearning program may generate the resident profile of the household. Theresident profile may include the number of residents of the household,the gender of the residents, the number and types of pets, and the like.

Based upon the patterns of activity, the machine learning program mayalso calculate or estimate a level of risk for a household. In oneembodiment, based upon the level of risk, or an adjusted risk profilefor the household, a premium or discount for a homeowner's insurancecomponent of the dynamic product may be calculated. The amount of thepremium or discount may vary according to the patterns of activitywithin the home, data from a resident profile, and/or data regardingfeatures of the house. The processing element 22 may also utilize themachine learning program and the resident profile to determine whenthere is a change in the residency of the household, such as theadoption of a child, the birth of a baby, the addition of a pet, themoving out of a child to an apartment or college, or the like. Theprocessing element 22 may further adjust the premium or discount for thedynamic product in response to changes in the residency profile.

A. Life & Health Product Components & Risk Profile

The machine learning may also relate to, inter alia, evaluatinginsurance applicants as part of an underwriting process to determineappropriate premiums and/or other terms of coverage. Broadlycharacterized, a processing element may be trained to probablisticallyanalyze still and/or moving (i.e., video) images and/or voice recordingsof applicants to determine personal and/or health-related informationfor an insurance provider. More specifically, the dynamicallyreconfigurable insurance product may include a life and/or healthinsurance component.

In one embodiment, an individual may provide, using their mobile device16 and one or more radio links, one or more still and/or moving (i.e.,video) images and/or voice recordings of him- or herself to a remoteserver or external computing device 14. The remote server or externalcomputing device 14 may include a processing element 22 and/or machinelearning program that analyze the image and/or audio data to determinepersonal and/or health-related information relevant to an underwritingprocess. The information may be used to determine whether and under whatterms, including appropriate premiums or discounts, the life or healthinsurance component of the overall dynamic product should be offered tothe applicant.

For instance, machine learning may involve identifying and recognizingpatterns in existing data in order to facilitate making predictions forsubsequent data. Models may be created based upon example inputs inorder to make valid and reliable predictions for novel inputs. Insupervised machine learning, the processing element 22 may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element 22 may be requiredto find its own structure in unlabeled example inputs. In oneembodiment, machine learning techniques may be used to extract therelevant personal and/or health-related information for insuranceapplicants from images and/or voice recordings of those applicantswithout needing to acquire samples of bodily fluids or conductconventional medical reviews.

In one embodiment, the processing element 22 may be trained by providingit with a large sample of otherwise non-diagnostic conventional analogand/or digital, still and/or moving (i.e., video) images and/or voicerecordings of persons with known personal and/or health-relatedinformation about the persons to analyze for correlations betweendetectable characteristics and the known information. Such informationmay include, for example, age, sex, weight, and height; tobacco,alcohol, and drug use; diet; existing medical conditions and riskfactors for future medical conditions; expected lifespan and cause ofdeath; and insurance premiums. Based upon these analyses, the processingelement 22 may learn how to identify characteristics and patterns thatmay then be applied to analyzing images of new insurance applicants. Forexample, the processing element 22 may learn to determine anindividual's pulse from a video of the applicant, may learn to identifymedication or other drug use by the applicant through, e.g., eyemovement, and/or may learn to determine such other information as theapplicant's glucose level. Similarly, the processing element 22 maylearn to identify indications of certain diseases, disorders, and/orbehaviors from a voice recording of the applicant.

Referring to FIG. 1, once trained using the sample data, the processingelement 22 may receive a still and/or moving (i.e., video) image and/orvoice recording of an insurance applicant and/or members of theirhousehold from their mobile device 16 over one or more radio links 19a-g, and may probablistically determine the personal and/orhealth-related characteristic for the insurance applicant and/or theirhousehold. The resulting data may be used to determine a risk level orprofile associated with a life and/or health insurance components of thedynamically reconfigurable insurance product. The resulting data mayalso verify information provided by the applicant and/or answeringunderwriting questions, and/or may be used to substantially automate theunderwriting process by directly predicting or estimating the actuallevel of risk and recommending a corresponding amount of insurancecoverage based upon the level or risk and/or other factors, such as ageand number of dependents. The applicant may then quickly be providedwith a rate quote for a new or updated dynamic product, such as viacommunication with their mobile device 16 over one or more radio links19 a-g.

The large sample of still and/or moving (e.g., video) images and/orvoice recordings used to train the processing element 22 may be, forexample, provided by volunteers. The still and/or moving (e.g., video)image and/or voice recording received from the applicant may be analogor digital and otherwise non-diagnostic and conventional in nature, suchas an ordinary “selfie” taken by the insurance applicant or him- orherself. The videos may include audio of the applicants' voices, and theprocessing element's 22 training and analysis may include similarlyseeking relevant characteristics or patterns in voices. The processingelement's 22 analyses of images may be probabilistic, such that theresulting data may be associated with varying degrees of certainty.

The processing element 22 may employ a neural network, which may be aconvolutional neural network (CNN) and/or a deep learning neuralnetwork. A CNN is a type of feed-forward neural network often used infacial recognition systems, in which individual neurons may be tiled soas to respond to overlapping regions in the visual field. A CNN mayinclude multiple layers of small neuron collections which examine smallportions of an input image, called receptive fields. The results ofthese collections may be tiled so that they overlap to better representthe original image, and this may be repeated for each layer. Deeplearning involves algorithms that attempt to model high-levelabstractions in data by using model architectures, with complexstructures or otherwise, composed of multiple non-lineartransformations. An image may be represented in various ways, such as avector of intensity values per pixel, a set of edges, or regions ofparticular shape. Certain representations may better facilitate learninghow to identify personal and health-related information from examples.

Thus, the present embodiments may be used to probablistically evaluateapplicants for life or health insurance components of the dynamicproduct and determine appropriate premiums or other terms of coveragebased upon analyses of still and/or moving images, and/or voicerecordings of the applicants and without requiring conventional medicalexaminations.

B. Homeowners or Renter Component & Risk Profile

In one aspect, a property may have a “smart” central controller that maybe wirelessly connected, or connected via hard-wire, with varioushousehold related items, devices, and/or sensors. The central controllermay be associated with any type of property, such as homes, officebuildings, restaurants, farms, and/or other types of properties. Thecentral controller may be in wireless or wired communication withvarious “smart” items or devices, such as smart appliances (e.g.,clothes washer, dryer, dish washer, refrigerator, etc.); smart heatingdevices (e.g., furnace, space heater, etc.); smart cooling devices(e.g., air conditioning units, fans, ceiling fans, etc.); smart plumbingfixtures (e.g., toilets, showers, water heaters, piping, interior andyard sprinklers, etc.); smart cooking devices (e.g., stoves, ovens,grills, microwaves, etc.); smart wiring, lighting, and lamps; smartpersonal vehicles; smart thermostats; smart windows, doors, or garagedoors; smart window blinds or shutters; and/or other smart devicesand/or sensors capable of wireless or wired communication. Each smartdevice (and/or sensor associated therewith), as well as the centralcontroller, may be equipped with a processor, memory unit, softwareapplications, wireless transceivers, local power supply, various typesof sensors, and/or other components.

The smart home controller 17 may be in communication with smartappliances. When new appliances are detected to have entered the home,or appliances detected to have left the home, a running virtualinventory of personal articles or personal belongings may be updated anddetermination made by the smart home controller 17 or external device 14of whether or not an increase or decrease in personal articles orhomeowners insurance is appropriate. The appliances may be identifiedby, for example, data that includes make and model information that istransmitted by the appliances, or object recognition or opticalcharacter recognition (or machine learning) techniques performed by thesmart home controller 17 or external device on image data received fromone or more cameras mounted about a home, such as a security system.

In other aspects, the sensors may include security systems, video orcameras that acquire image and/or audio data, and/or motion or infraredsensors which detect when people or animals are moving inside or outsidethe house. The sensor data (e.g., image or infrared data) may becollected over time at the smart home controller 17 and/or externaldevice or server 14, and used to determine when an additional person orpet has moved in, or left the household.

In one embodiment, facial recognition techniques may be performed on theimage data received from one or more home-mounted cameras to identifymembers of the household and when the composition of the household haschanged. For instance, when person is identified as not belonging to thecurrent household, an analysis may be performed of time-stamped imagesto determine how often that person is in the home and when—such as, aperson that is routinely sleeping at the home during night time hours islikely a new member of the household. Conversely, when it is determinedthat a member of the household has not been captured within images takenby the home-mounted cameras for a given amount of time, such as a month,then it may be determined that that person is no longer, or likely nolonger, a member of the household.

The smart home controller 17 and/or external device or server 14 mayalso analyze time-stamped image data to determine or estimate an amounttime during an average day or week that the home is occupied orunoccupied, and time of day or night that the home is occupied orunoccupied. Based upon the number of home occupants and/or the amount oftime in each day that someone is at home (or the home is unoccupied),the smart home controller 17 and/or external device or server 14 maydetermine a risk profile for the household and/or that an increase ordecrease in a homeowners or renters insurance component of the dynamicproduct is warranted or recommended.

In one embodiment, the sensor data may be transmitted from the sensorsto a central hub or smart home controller 17 which forwards the data toan external computing device 14 via one or more radio links 19 a-19 g.At various intervals, the external computing device 14 may generate areport that summarizes the sensor data. The external computing device 14may also receive data derived from dynamically reconfigurable insuranceproduct. The data from the report and the dynamic reconfigurable productmay be input into a machine learning program. The program may recognizepatterns of activity from the sensor data and may generate a residentprofile, or risk level or score. The resident profile along with dataregarding the features of the house, household, and/or individual may beutilized to calculate a premium for the dynamically reconfigurableproduct, and/or propose adjustments coverages, deductibles, or limitsand/or changes to types of insurance (such as homeowners, renters, orpersonal articles insurance) within the dynamic product for theindividual's or customer's review and approval.

In another aspect, machine learning (as discussed elsewhere herein) maybe applied to the smart home data, such as image data, collected andgenerated from various home-mounted sensors (e.g., security cameras) todetermine one or more home features. The home features may includenumber of rooms; size of rooms; type of rooms (kitchen, bath, masterbedroom, etc.); type of flooring, windows, ceilings, cabinets, andcounter tops; age and type of roof; type of exterior; type of basement(finished or not); type of security system; etc.). The machine learningtechniques may be used to extract the relevant home feature orcharacteristic-related information for homeowners insurance from imagesand/or other data collected from home sensors or electronic devices.

For instance, the processing element 22 may be trained by providing itwith a large sample of conventional analog and/or digital, still and/ormoving (i.e., video) images of homes with known features orcharacteristics to analyze for correlations between detectable featuresor characteristics and the known information. Such information mayinclude, for example, type of flooring, cabinets, ceilings, countertops, windows, roofing, age of home or roof, etc. Based upon theseanalyses, the processing element 22 may learn how to identify homecharacteristics and patterns that may then be applied to analyzingimages of new homes.

Once trained using the sample data, the processing element 22 mayreceive a still and/or moving (i.e., video) image of home of aninsurance applicant from their mobile device 16 or smart home controller17 over one or more radio links 19 a-g, and may probablisticallydetermine one or more home features or characteristics. The resultingdata may be used to determine a risk level or profile associated withhomeowners or renters insurance component of the dynamicallyreconfigurable insurance product. The dynamic product may then begenerated or adjusted based upon risk level or profile associated withthe home or apartment.

C. Dynamic Personal Articles Component & Risk Profile

The methods and systems may include a unique combination of processesand technology, based upon, for example, an object recognition (OR)algorithm (and/or optical character recognition (OCR) algorithm) orother machine learning technique that automatically identifies apersonal article using at least one digital image of the personalarticle. For example, a personal article category (e.g., jewelry,computers, tools, firearms, etc.) may be determined by using an objectrecognition algorithm to process at least one digital image of thepersonal article. The methods and systems may automatically identify atleast one document (e.g., an appraisal, a purchase receipt, etc.)associated with a personal article using, for example, an opticalcharacter recognition (OCR) algorithm.

The methods and systems may validate that, for example, an appraisalmatches an associated personal article. The methods and systems mayvalidate that a personal article is authentic. An object or opticalcharacter recognition (or other machine learning) technique mayautomatically identify data attributes contained within document(s)associated with a personal article. The systems and methods may collectdata (e.g., appraisal dates, quality measures, dollar amounts, etc.)associated with a personal article. Metadata may be extracted from datathat is representative of at least one digital image of a personalarticle, and may be collected for potential use in data pre-population,underwriting, fraud detection, etc. Date/time stamps, geo-tags, etc. mayalso be extracted from data that is representative of at least onedigital image of a personal article. The methods and systems may includeprocesses that pre-populate the extracted data into workflows.

The processing element 22 may execute an object recognition/opticalcharacter recognition (or other machine learning) module, which may besoftware application 26, to extract personal articles information datafrom digital images of personal belongings, and/or the digital images ofthe supporting documentation (such as appliance owner's manuals)—thedigital images received from the smart home controller 17 or mobiledevice 16 via one or more radio links.

Processing element 22 may execute a personal articles insurance policyuser interface module to cause the processing element 22 to generate apersonal articles inventory based upon one or more of an identifiedtype, an identified characteristic and/or an estimated value. Theinventory may be associated with a specific insured or family, and mayinclude the type and features of various belongings, their estimatedvalue, and/or their purchase or replacement cost.

Processing element 22 may execute an insurance-related action to beperformed based upon the personal articles inventory. For instance, theprocessing element 22 may generate or adjust a quote, premium, discount,or policy for a personal articles insurance component of the dynamicproduct based upon the inventory or an updated inventory. Processingelement 22 may then transmit the updated dynamic product to the customerfor their review, approval, or modification, such as via wirelesscommunication or data transmission with the insured's mobile device.

D. Determining Life Events

A computer system for generating or updating the dynamic product basedupon life events and/or life event data may include a client device 16or smart home controller 17 in communication with a remote computerdevice (e.g., a server) 14 via a network 12. The computer system mayacquire life event data from, for example, a user of a client or mobiledevice 16 (e.g., a smart phone, a digital camera, smart watch, smartglasses, wearable electronics, laptop, smart vehicle, etc.).Alternatively, or additionally, life event data may be automaticallyobtained from a third party data source (e.g., a bureau of motorvehicles, a court, a country, a state, a county, a local municipality, agovernment agency, a utility provider, a cable company, a phone company,etc.) with the permission of the insured, such as receiving anotification from the insured that they would like to opt-in to aninsurance program that automatically provides insurance savings to riskaverse customers and/or recommendations based upon life events.

As described in detail herein, the computer system may automaticallygenerate or revise the dynamic product based upon, for example, lifeevents and/or life event data. The life events and/or life event data,may be representative, for example, a marriage, a divorce, a childbirth, a name change, a vehicle purchase, a vehicle sale, a housepurchase, a home sale, an adoption, a change in employment, a change inemployer, a move into a new apartment or home, a move to a differentstate, etc.

Processing element 22 may execute a module or software application 26(that may employ a machine learning technique) to cause the processingelement 22 to automatically detect an actual life event from analysis ofdata received from a data source (e.g., a bureau of motor vehicles datasource, a court data source, a marriage records data source, anobituaries data source, a government agency data source, etc.), such aswith an insured's permission to monitor for life events that may providethem with insurance recommendations and/or insurance cost savings.Alternatively, or additionally, processing element 22 may execute amodule or software application 26 (that may employ a machine learningtechnique) to cause the processing element 22 automatically predict alife event from data mined from at least one data source (e.g., aninsurance provider data source), such as with an insured's permission tomonitor for life events that may provide them with insurancerecommendations and/or insurance cost savings.

The processing element 22 may execute an automatic life event datageneration module to cause the processing element 22 to automaticallyupdate a risk profile or level for a component of the dynamic product oran overall risk profile for the dynamic profile. For instance, thecomponents of the dynamic product may include homeowners, auto, life,health, and personal articles insurance components. The processingelement 22 may determine a life event from applying a machine learningor other algorithm to the data collected via the network 12, and thenmay determine an impact of the life event on an insured, a risk profilefor the insured, and/or the dynamic product. The processing element 22may then create a new, or update an existing, dynamic product.

The processing element 22 may execute a module or software application26 that causes the processing element 22 to automatically determine ifthere is at least one gap in insurance coverage for an existinginsurance customer based upon the life event data. For instance, theprocessing element 22 may determine that there is a need for additionalinsurance based upon the purchase, or impending purchase, of a newvehicle, new personal articles, or a new home, or the birth of a childor a marriage.

E. Telematics Data and Auto Component

Telematics data, as used herein, may include telematics data, and/orother types of data that have not been conventionally viewed as“telematics data.” The telematics data may be generated by, and/orcollected or received from, various sources. For example, the data mayinclude, indicate, and/or relate to vehicle (and/or mobile device)speed; acceleration; braking; deceleration; turning; time; GPS (GlobalPositioning System) or GPS-derived location, speed, acceleration, orbraking information; vehicle and/or vehicle equipment operation;external conditions (e.g., road, weather, traffic, and/or constructionconditions); other vehicles or drivers in the vicinity of an accident;vehicle-to-vehicle (V2V) communications; vehicle-to-infrastructurecommunications; and/or image and/or audio information of the vehicleand/or insured driver before, during, and/or after an accident. The datamay include other types of data, including those discussed elsewhereherein. The data may be collected via wired or wireless communication.

The data may be generated by mobile devices (smart phones, cell phones,lap tops, tablets, phablets, PDAs (Personal Digital Assistants),computers, smart watches, pagers, hand-held mobile or portable computingdevices, smart glasses, smart electronic devices, wearable devices,smart contact lenses, and/or other computing devices); smart vehicles;dash or vehicle mounted systems or original telematics devices; publictransportation systems; smart street signs or traffic lights; smartinfrastructure, roads, or highway systems (including smartintersections, exit ramps, and/or toll booths); smart trains, buses, orplanes (including those equipped with Wi-Fi or hotspot functionality);smart train or bus stations; internet sites; aerial, drone, or satelliteimages; third party systems or data; nodes, relays, and/or other devicescapable of wireless RF (Radio Frequency) communications; and/or otherdevices or systems that capture image, audio, or other data and/or areconfigured for wired or wireless communication.

In some embodiments, the data collected may also derive from police orfire departments, hospitals, and/or emergency responder communications;police reports; municipality information; automated Freedom ofInformation Act requests; and/or other data collected from governmentagencies and officials. The data from different sources or feeds may beaggregated.

The telematics and other data generated may be transmitted, via one ormore radio links, to a remote server or external computing device 14,such as a remote server and/or other processor(s) associated with aninsurance provider. The remote server 14 and/or associated processorsmay build a database of the telematics and/or other data, and/orotherwise store the data collected.

The remote server 14 and/or associated processors may analyze the datacollected, such as via a machine learning technique, and then performcertain actions and/or issue tailored communications based upon thedata, including adjusting an auto insurance component of the dynamicproduct. The automatic gathering and collecting of data from severalsources by the insurance provider, such as via one or more radio links,may prompt adjustment to auto-related risk levels or profiles, and/or anauto component within the dynamic product, and may include the automaticidentification of insured events, and/or the automatic or semi-automaticprocessing or adjusting of insurance claims.

In one embodiment, telematics data may be collected by a mobile device(e.g., smart phone) application. An application that collects telematicsdata may ask an insured for permission to collect and send data aboutdriver behavior and/or vehicle usage to a remote server 14 associatedwith an insurance provider. The remote server 14 may be trained toidentify risk averse driving behavior, and a machine learning programrunning on the remote server 14 may adjust a risk factor for the insuredbased upon the telematics data input into the machine learning program.In return, the insurance provider may provide incentives to risk averseinsureds, such as lower premiums or rates, or discounts.

The remote server 14 may analyze the collected telematics data, using amachine learning technique, to determine driver driving behavior,driving characteristics and/or driving environments. Informationregarding driving characteristics may include indicators of aggressiveor conservative driving, such as speed, braking (hard, soft, frequency,etc.), acceleration, lane centering, distance from other vehicles,attentiveness, distraction, fatigue, impairment, and/or use of vehicleoptions or equipment. Information regarding driving environments mayinclude time, location, type of road, traffic or congestion, weatherconditions, construction, and/or other relevant information regardingthe operating environment of the vehicle. The driving characteristicand/or driving environment information may be classified in categoriesand/or scored (e.g., by determining probabilities or likelihoods ofsalient features). In some embodiments, machine learning techniques maybe used to determine driving characteristics and/or drivingenvironments, and then update one or more risk profiles for a driver,and/or generate an updated dynamic product for the driver—all reflectiveof the telematics data collected.

To determine a driving risk or driving risk score, the remote server 40may further analyze and/or process the insured driver driving behaviordata to determine insured driver driving characteristics and/or typicalacuity, normal driving conditions for the insured (including road,weather, construction, and/or traffic), and/or other insured or vehiclecharacteristics (including vehicle maintenance records). In someembodiments, a plurality of driving risk scores may be determined, whichmay be associated with different driving environments, different insureddrivers, and/or different vehicles. In some embodiments, the drivingrisk or driving risk score may also include one or more risk aversionscores indicating a general risk preference profile or level of theinsured driver.

The driving risk or driving risk score of the insured may be applied toan automobile insurance component of the dynamic product at or via theremote server 14. This may include adjusting, updating, and/orgenerating automobile or automotive insurance component based upon thedetermined driving risks or driving risk scores, which may furtherinclude adjusting, updating, determining, applying, and/or implementingpremiums, rates, discounts, surcharges, deductibles, limits, and/orother terms of one or more insurance policies, which terms may berelated to price and/or coverage.

The driving risk or driving risk score of the insured may also beapplied to one or more non-auto insurance (e.g., health, life, homeowners, renters, etc.) components of the dynamic product at the remoteserver 14. This may include determining associations and/or correlationsbetween the data and risk preferences and/or levels associated with oneor more insurance customers and/or insured persons. For example, drivingrisk scores may be indicative of general risk preferences, which mayfurther affect risk levels relating to health, life, or propertyinsurance policies. Changes to the one or more non-automobile insurancecomponents may cause the dynamic product to more accurately reflect therisk levels determined from the telematics and/or other data. To thisend, the server 14 may weight the driving risk scores and/or other riskscores based upon the type of insurance policy to be adjusted.

F. Data Units

In one embodiment, the processing element 22, and/or machine learningtechniques discussed herein, may compare time-stamped data units of datacollected over time to determine certain risk factors. For instance,time-stamped data units of GPS location acquired from an individual'smobile device may be analyzed by the processing element 22 to determinewhere they typically sleep to determine their current residence. Theircurrent residence may be compared with a previous residence—the previousresidence being determined from historic or older time-stamped dataunits of their mobile device GPS location(s). If the current residenceis not the same as the previous residence after a given amount of time,an adjustment to a risk profile associated with their residence, and/orto the homeowners or renter's insurance component of the dynamic productmay be warranted.

Similarly, time-stamped data units of GPS location acquired from anindividual's mobile device 16 via one or more radio links 19 b, 19 e maybe analyzed by the processing element 22 to determine where theytypically spend their waking hours, which may indicate a current placeof employment or school. Their current employer or level of educationmay be compared with a previous employer or level of educationdetermined from historic or older time-stamped data units of theirmobile device GPS location(s). A change in employment or school, mayindicate a change in risk for the individual, such as a change incommute or a previously undetected change in residence—which may in turnwarrant a change in the dynamic product.

Additionally, time-stamped data units of images of people or animalsacquired from a smart home controller 17 via one or more radio links 19a, 19 e may be analyzed by the processing element 22 to determine numberof people residing in the home, and a type and number of pets residingin the home as well. The current data units of resident images may becompared with previous data units of resident images to determine achange in a household members. The change in household members mayindicate an adjustment to a risk profile associated with the residenceor household, and/or to the homeowners or renter's insurance componentof the dynamic product may be warranted.

Exemplary Dynamically Reconfigurable Product

FIG. 4 illustrates an exemplary dynamically reconfigurable insuranceproduct 400 that changes over time based upon, at least in part,computer server 14 analysis, with customer permission or affirmativeconsent, of customer or customer-related data, such as after customeropt-in to an insurance discount or rewards program. The dynamic product400 may be originally purchased 402 with a single type of insurance,such as auto, and as the customer's needs change, additional types ofinsurance may be added—life, renters, homeowners, etc. Alternatively,the dynamic product 400 may be originally purchased 402 with more thanone type of insurance, such as auto, homeowners, and life insurance tostart. Still further, the dynamic product 400 may be originallypurchased without definition according to traditional, discreteinsurance policies. Put another way, the dynamic product 400 maycomprise a need-based, comprehensive policy unbounded by the traditionaloutlines applicable to insurance policies. As the customer needschanges, such as at each life event 404, the coverage levels (ordeductibles or limits) for each type of insurance may increase ordecrease, and/or different types of insurance may be added ordropped—such as with the customer's pre-approval or post-approval.

For instance, life events 404 may include getting a driver's license;getting married or divorced; having or adopting a child; having a childget a driver's license; graduating college or high school; having achild graduate high school or college; medical events, illnesses, orsurgeries; a death in the family; moving (such as moving from anapartment to a house, or vice versa); turning a certain age (e.g., 25years old); retirement; and/or other life events. In addition to lifeevents 404, the customer and/or customer-related data (including datagenerated by or received from the customer's vehicle or mobile device,and/or information gleaned from scanning social media posts (all withthe customer's prior authorization or consent)) may be analyzed todetermine customer activity or location, such as determining that thecustomer is traveling or on vacation. The different types of insurancewithin the dynamic product 400 may be adjusted automatically based uponthe life event, customer or customer-related data, and/or activity orlocation of the customer. Additionally or alternatively, a proposedchange to dynamic product may be communicated to the customer's mobiledevice via wireless communication or data transmission for their review,modification, and/or approval.

Exemplary Adjustment Based Upon Life Event

FIG. 5 illustrates an exemplary computer-implemented method 500 ofadjusting a dynamically reconfigurable insurance product based upon, atleast in part, customer or customer-related data, and/or life event(and/or customer activity or traveling) detection. The steps may beperformed in the order shown in FIG. 5, or they may be performed in adifferent order. Furthermore, some steps may be performed concurrentlyas opposed to sequentially. In addition, some steps may be optional. Thesteps of the computer-implemented method 500 may be performed by thesystem 10.

The method 500 may include selling a dynamically reconfigurableinsurance product 502; collecting and/or analyzing 504 customer-relateddata at or via server 14; determining or detecting a life event (orcustomer activity) at or via the server 14 from computer analysis of thecustomer-related data 504; automatically adjusting 508 the dynamicallyreconfigurable insurance product, scope, and/or an associated insurancepremium, discount, insurance coverage, etc. at or via the server 14based upon, at least in part, the type of life event (or customeractivity or traveling); generating and transmitting 510 a communicationor notification regarding the adjustment (or even a proposed adjustment)to the dynamic insurance product to the customer mobile device 18 fromthe server 14; causing the notification to be presented 512 on thecustomer's mobile device 18 for the customer's review, approval, orrejection; and/or receiving 514 a customer's approval or rejection ofthe changes, or recommended changes or coverages, from the customer'smobile device 18 at the server 14.

The method 500 may include selling 502 a dynamically reconfigurableinsurance product. The dynamic insurance product may originally includeone or more types of insurance coverage. Over time, other types ofinsurance coverage may be added or dropped. Also over time, coverages,limits, or deductibles associated with the different types of insurancemay be adjusted, such as discussed elsewhere herein.

The method 500 may include collecting and/or analyzing 804 insurancecustomer or customer-related data at a server 14. Customer orcustomer-related data may be generated from various types of sensors,including those associated with the customer's mobile device(s) 18,home, vehicle, and/or home computer. The data may be transmitted to aninsurance provider server 14 or processor via wired or wirelesscommunication and/or data transmission from a transceiver associatedwith the customer's mobile device 18, home (such as a smart homecontroller), vehicle (such as a smart vehicle controller), and/or homecomputer.

The method 500 may include determining or detecting 506 a life event(and/or other customer activity) at the server 14 from computer analysisof the customer or customer-related data. For instance, from analysis ofthe data, it may be determined that the insured is buying or has boughta vehicle or home, is getting married, is expecting a child, is moving,is planning a trip or vacation, and/or about to experience, or hasexperienced, an event that has changed their insurance needs. In oneembodiment, the customer or customer-related data (including mobiledevice, vehicle, or home-mounted sensor data and/or telematics data) maybe input into a trained machine learning program to determine the lifeevent(s) or other customer activity. Object recognition, facialrecognition, and/or optical character recognition techniques may be alsobe applied to the data collected to determine life events or othercustomer activity.

The method 500 may include automatically adjusting 508 the dynamicallyreconfigurable insurance product and/or an associated insurance premium,discount, coverage, deductible, limit, etc. at the remote server basedupon, at least in part, the type of life event (or other activity)detected. For instance, when a home is purchased, the dynamic insuranceproduct may automatically be adjusted to include an appropriate amountof homeowners insurance for the insured, or if a new vehicle ispurchased, that vehicle may be automatically insured at a recommended orappropriate level. The adjustments to the dynamically reconfigurableinsurance product may be permanent and immediately binding and legallyenforceable. Alternatively, the adjustments to the dynamicallyreconfigurable insurance product may be temporary, such as dependentupon customer review and approval.

The method 500 may include generating and transmitting 510 acommunication or notification regarding the adjustment (or proposedadjustment) to the dynamic insurance product to the customer's mobiledevice 18 from the server 14. An insurance provider server 14 maygenerate a notification of changes to the dynamic insurance product,and/or proposed changes to the dynamic insurance product. The insuranceprovider server 14 may then transmit the notification, via wirelesscommunication or data transmission, to the insured's mobile device 18,vehicle, home, or other computing device.

The method 500 may include causing 512 the notification to be presentedon the customer's mobile device 18 for the customer's review, approval,or rejection. The notification regarding the changes or proposedchanges, or other insurance recommendations, may be presented on amobile device 18, computing device, vehicle, or home display screen forthe customer's approval or rejection, for example as instructed bysoftware application 34.

The method 500 may include receiving 514 a customer's approval orrejection of the changes, or recommended changes or coverages from thecustomer's mobile device 18 at the server 14. The recommendations mayinclude adding or removing certain types of insurance to or from,respectively, the dynamic insurance product. Once the customer'sapproval or rejection of the changes or recommended changes to thedynamic insurance product are received at the insurance provider server14, such as via wireless communication or data transmission from acustomer's mobile device 18, vehicle, home, or other computing device,the insurance provider remote server may update the dynamic insuranceproduct, and insurance coverages and associated billing, accordingly.

The method 500 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Adjustment of Dynamic Product

FIG. 6 illustrates an exemplary computer-implemented method 600 ofdetermining a type of insurance to adjust within a dynamicallyreconfigurable insurance product having several types of insurance basedupon customer data and/or life event detection. The steps may beperformed in the order shown in FIG. 6, or they may be performed in adifferent order. Furthermore, some steps may be performed concurrentlyas opposed to sequentially. In addition, some steps may be optional. Thesteps of the computer-implemented method 600 may be performed by thesystem 10.

The method 600 may include selling 602 a dynamically reconfigurableinsurance product (such as discussed with respect to FIG. 5 and/orelsewhere herein); with customer permission, collecting and/or analyzing604 insurance customer or customer-related data (and/or GPS data) atserver 14; determining or detecting 606 a life event (or other customeractivity) at the server 14 from computer analysis of the customer orcustomer-related data; based upon, at least in part, the type of lifeevent (or other customer activity) determined or detected, determining608 a type of insurance to adjust and/or determining an amount ofcoverage to add or remove from the dynamically reconfigurable insuranceproduct; determining 610 an updated or recommended premium, discount,rate, coverage, deductible, etc. at the server 14; generating and/ortransmitting 612 a notification regarding the adjustment (or proposedadjustment or insurance recommendation) to the dynamic insurance productto the customer mobile device 18 from the server 14; causing 614 thenotification to be presented on the customer's mobile device 18 for thecustomer's review, approval, or rejection; and/or receiving 616 acustomer's approval or rejection of the changes, or recommended changesor coverages (and/or changes or recommended changes to the types ofinsurance contained with the dynamic insurance product) from thecustomer's mobile device 18 at the server 14.

The method 600 may include with customer permission, collecting and/oranalyzing 604 insurance customer or customer-related data (and/or GPSdata) at a server 14. For instance, customer data, including GPS data,may be generated by a customer mobile device 18 or vehicle, and receivedat insurance provider server 14, such as via wireless communicationand/or data transmission.

The method 600 may include determining or detecting 606 a life event (orother customer activity) at the server 14 from computer analysis of thecustomer or customer-related data. The server 14 may detect life eventsor customer activity (such as vacations or trips, or travel movement),such as discussed elsewhere herein. In one embodiment, the customer orcustomer-related data (including mobile device, vehicle, or home-mountedsensor data, telematics data, and/or sensor GPS data) may be input intoa trained machine learning program to determine the life event(s) orother customer activity.

The method 600 may include based upon, at least in part, the type oflife event (or other customer activity) determined or detected,determining 608 a type of insurance to adjust and/or determining anamount of coverage to add or remove from the dynamically reconfigurableinsurance product. For instance, if it is determined that the customerneeds travel insurance and/or a reduction in auto insurance, the dynamicinsurance product may be adjusted accordingly.

The method 600 may include determining 610 an updated or recommendedpremium, discount, rate, coverage, deductible, etc. at the server 14.For instance, as discussed elsewhere herein, the a specific portion ofthe dynamic insurance product premium may be scaled up or down basedupon increased or decreased risk, and/or a new or reduced level ofcustomer need.

The method 600 may include generating and/or transmitting 612 anotification regarding the adjustment (or proposed adjustment orinsurance recommendation) to the dynamic insurance product to thecustomer mobile device 18 from the server 14. For instance, the server14 may provide a notification to the customer mobile device 18, vehicle,home, or other computing device via wireless communication or datatransmission.

The method 600 may include causing 614 the notification to be presentedon the customer's mobile device 18 for the customer's review, approval,or rejection. For instance, the server 14 may cause the notification tobe electronically available to the customer after the customer logs intoa secure insurance provider website.

The method 600 may include receiving 616 a customer's approval orrejection of the changes, or recommended changes or coverages (and/orchanges or recommended changes to the types of insurance contained withthe dynamic insurance product) from the customer's mobile device 18 atthe server 14. For instance, the customer's mobile device 18 maywirelessly communicate with an insurance provider's server 14 or aninsurance provider's secure website.

The method 600 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Addition to Dynamic Product

FIG. 7 illustrates an exemplary computer-implemented method 700 ofdetermining a new type of insurance to add to a dynamicallyreconfigurable insurance product having several types of insurance basedupon customer data and/or life event detection. The steps may beperformed in the order shown in FIG. 7, or they may be performed in adifferent order. Furthermore, some steps may be performed concurrentlyas opposed to sequentially. In addition, some steps may be optional. Thesteps of the computer-implemented method 700 may be performed by thesystem 10.

The method 700 may include selling 702 a dynamically reconfigurableinsurance product (such as discussed elsewhere herein); collectingand/or analyzing 704 insurance customer or customer-related data at aserver 14 (such as discussed elsewhere herein); and/or determining ordetecting 706 a life event (or other customer activity) at the server 14from computer analysis of the customer-related data (such as discussedelsewhere herein). In one embodiment, the customer or customer-relateddata (including mobile device, vehicle, or home-mounted sensor dataand/or telematics data) may be input into a trained machine learningprogram to determine the life event(s) or other customer activity.Object recognition, facial recognition, and/or optical characterrecognition techniques may be also be applied to the data collected todetermine life events or other customer activity.

The method 700 may further include, based upon the life event and/orother customer activity detected, determining 708 a new type ofinsurance to add to the dynamically reconfigurable insurance product atthe server 14; determining 710 an updated premium, discounts, rates,coverages, deductibles, etc. at the server 14; generating andtransmitting 712 a communication or notification regarding theadjustment (or proposed adjustment) to the dynamic insurance product tothe customer mobile device 18 from the server 14 (such as discussedelsewhere herein); causing 714 the notification to be presented on thecustomer's mobile device 18 for the customer's review, approval, orrejection (such as discussed elsewhere herein); and/or receiving 716 acustomer's approval or rejection of the changes, or recommended changesor coverages from the customer's mobile device 18 at the server 14 (suchas discussed elsewhere herein).

For instance, the method 700 may include, based upon (at least in part)the life event and/or other customer activity detected, determining 708a new type of insurance to add to the dynamically reconfigurableinsurance product at the server 14. For instance, for an insured thathas bought a first vehicle, auto insurance coverage may be added to thedynamic insurance product. For an insured that bought a first house,homeowners insurance may be added. For an insured with addedresponsibility, such as because of a marriage or a birth of a child,life insurance may be added or increased. For an insured that has juststarted renting an apartment, renters insurance may be added. For aninsured that has bought, or is about to buy, an expensive piece ofproperty (an antique or engagement ring), personal articles insurancemay be added or increased. For an insured that has added a pet to thefamily, pet insurance may be added or increased. For an insured that istraveling, temporary, or even permanent, travel insurance may be addedor increased. Other types of insurance may be added to and/or removedfrom the dynamic insurance product, as well as increased or decreased incoverage amount.

The method 700 may include determining 710 an updated premium, discount,rate, coverage, deductible, etc. at the server 14. For instance, for theinsurance types or coverage added or removed, based upon, at least inpart, analysis of the customer or customer-related information, updatedpremiums or discounts may be calculated by the server 14, and/or appliedto the dynamic insurance product.

The method 700 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Adjustment Based Upon Location

FIG. 8 illustrates an exemplary computer-implemented method 800 ofadjusting a dynamically reconfigurable insurance product based uponcustomer location or GPS data, and determining likely activity and/orinsurance needs from the customer location or GPS data (and/or lifeevent detection). The steps may be performed in the order shown in FIG.8, or they may be performed in a different order. Furthermore, somesteps may be performed concurrently as opposed to sequentially. Inaddition, some steps may be optional. The steps of thecomputer-implemented method 800 may be performed by the system 10.

The method 800 may include selling 802 a dynamically reconfigurableinsurance product (such as discussed elsewhere herein); collectingand/or analyzing 804 insurance customer or customer-related data(including GPS or location) at a server 14 (such as disclosed elsewhereherein); and/or determining or detecting 806 a life event (or customerlocation or other customer activity/movement) at the server 14 fromcomputer analysis of the customer-related data (such as discussedelsewhere herein). In one embodiment, the customer or customer-relateddata (including mobile device, vehicle, or home-mounted sensor dataand/or telematics data) may be input into a trained machine learningprogram to determine the life event(s) or other customer activity.Object recognition, facial recognition, and/or optical characterrecognition techniques may be also be applied to the data collected todetermine life events or other customer activity.

The method 800 may include automatically adjusting 808 the dynamicallyreconfigurable insurance product and/or associated insurance premium,discount, coverages, etc. at the server 14 based upon, at least in part,the customer location data (and/or type of life event or detectedcustomer movement); determining 810 an updated premium, discount, rate,coverage, etc. at the server 14 based upon, at least in part, thecustomer location data (and/or type of life event or detected customermovement); generating and transmitting 812 a communication ornotification regarding the adjustment (or proposed adjustment) to thedynamic insurance product to the customer mobile device 18 from theserver 14 (such as discussed elsewhere herein); causing 814 thenotification to be presented on the customer's mobile device 18 for thecustomer's review, approval, or rejection (such as discussed elsewhereherein); and/or receiving 816 a customer's approval or rejection of thechanges, or recommended changes or coverages (and/or changes orrecommended changes to the types of insurance contained within thedynamic insurance product, and/or the various coverage amounts) from thecustomer's mobile device 18 at the server 14.

For instance, the method 800 may include automatically adjusting 808 thedynamically reconfigurable insurance product and/or associated insurancepremium, discount, coverages, etc. at the server 14 based upon thecustomer location data (and/or type of life event or detected customermovement). The dynamic insurance product may be adjusted based upon, atleast in part, customer location data, such as GPS data received from acustomer mobile device 18 or vehicle. As an example, if it is determinedthat the customer is taking a trip and not using their personal vehicle,their auto insurance rate may be dynamically adjusted, and/or thedynamic insurance product may be automatically adjusted to includetravel insurance. Other adjustments to the dynamic insurance product mayalso be made, including those discussed elsewhere herein.

The method 800 may include determining 810 an updated premium, discount,rate, coverage, etc. at the server 14 based upon, at least in part, thecustomer location data (and/or type of life event or detected customermovement). For instance, various types of insurance (auto, home,renters, life, travel, etc.) may be adjusted in real-time or nearreal-time based upon GPS information received from the customer's mobiledevice 18 or other computing device, such as discussed elsewhere herein.Other adjustments may be made based upon customer or customer deviceinformation.

The method 800 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Adjustment of Reconfigurable Product

FIG. 9 illustrates an exemplary computer-implemented method 900 ofreconfiguring a dynamically reconfigurable insurance product based uponcustomer input or engagement, customer data and/or situation changedetection. The steps may be performed in the order shown in FIG. 9, orthey may be performed in a different order. Furthermore, some steps maybe performed concurrently as opposed to sequentially. In addition, somesteps may be optional. The steps of the computer-implemented method 900may be performed by the system 10.

The method 900 may include acquiring 902 a dynamically reconfigurableinsurance product (such as discussed with respect to FIGS. 5-8 and/orelsewhere herein); receiving 904 customer input or engagement and/or theresults of an analysis of customer data at server 14; with customerpermission, analyzing and/or determining 906 customer input orengagement and/or the results of an analysis of customer data at server14 to determine if a situation change has occurred (such as employing orapplying machine learning, object recognition, optical characterrecognition, or facial recognition techniques or algorithms to thecustomer data); determining 908 at the server 14 whether a situationchange impacts the terms of the dynamically reconfigurable insuranceproduct; if the answer is no, halting further analysis based upon thecustomer input or engagement and/or results of analysis of customer dataunder consideration (not shown); if the answer is yes, analyzing 910 atserver 14 the impact of the situation change on the dynamicallyreconfigurable insurance product to determine 912 whether the situationchange meets the conditions for reconfiguration of the product—if not,further analysis is halted as described immediately above; if the answerto the inquiry at step 912 is yes, determining 914 whether customerapproval is needed for a proposed reconfiguration determined based uponthe analysis of step 912; determining 914 whether customer approval isneeded may include considering a previously-entered customer preference916; where approval is needed, generating and transmitting anotification regarding, or prompting 918 the customer to approve, theadjustment (or proposed adjustment or insurance recommendation) to thedynamic insurance product to the customer mobile device 18 from theserver 14; if the customer approves pursuant to step 918 or if customerapproval is not needed, reconfiguring 920 the dynamically reconfigurableproduct; determining 922 whether the reconfiguration requires a changeor adjustment to any terms governing the dynamically reconfigurableinsurance product; and updating or re-affirming the continued vitality924 of a product agreement and/or terms associated with the dynamicallyreconfigurable insurance product in light of the reconfiguration at step920.

The method 900 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Customer-Related Data

The customer and/or customer-related data discussed herein may includedata stored in an insurance provider server 14 (such as customerinformation—name, age, marital status, dependents, address, employmentstatus, financial income, financial accounts, account balances (such assavings, checking, trading, credit or debit card, or mutual fund accountbalances), etc.), and/or dynamic data collected by a customer's mobiledevice 18, smart home controller, smart vehicle, various sensors orsensor arrays, and/or other customer computing device.

In one embodiment, the customer and/or customer-related data may alsoinclude telematics data, such as telematics data collected by a smartvehicle, mobile device 18 or mobile device application 34, or aconventional dashboard plug-in telematics device. The telematics data,as used herein, may include telematics data, and/or other types of datathat have not been conventionally viewed as “telematics data.” Thetelematics data may be generated by, and/or collected or received from,various sources. For example, the data may include, indicate, and/orrelate to vehicle (and/or mobile device) speed; vehicle mileage and/orvehicle usage, vehicle acceleration, braking, deceleration, turning, orcorning time; GPS (Global Positioning System) or GPS-derived location,speed, acceleration, or braking information; vehicle and/or vehicleequipment operation; external conditions (e.g., road, weather, traffic,and/or construction conditions); other vehicles or drivers in thevicinity of an accident; vehicle-to-vehicle (V2V) communications;vehicle-to-infrastructure communications; and/or image and/or audioinformation of the vehicle and/or insured driver before, during, and/orafter an accident.

The customer or customer-related data may include other types of data,including those discussed elsewhere herein. The data may be collectedvia wired or wireless communication with customer permission.

The customer or customer-related data may be generated by mobile devices18 (smart phones, cell phones, lap tops, tablets, phablets, PDAs(Personal Digital Assistants), computers, smart watches, pagers,hand-held mobile or portable computing devices, smart glasses, smartelectronic devices, wearable devices, smart contact lenses, and/or othercomputing devices); smart vehicles; dash or vehicle mounted systems ororiginal telematics devices; public transportation systems; smart streetsigns or traffic lights; smart infrastructure, roads, or highway systems(including smart intersections, exit ramps, and/or toll booths); smarttrains, buses, or planes (including those equipped with Wi-Fi or hotspotfunctionality); smart train or bus stations; internet sites; aerial,drone, or satellite images; third party systems or data; nodes, relays,and/or other devices capable of wireless RF (Radio Frequency)communications; and/or other devices or systems that capture image,audio, or other data and/or are configured for wired or wirelesscommunication.

In some embodiments, the customer data, customer-related data, and/ortelematics data collected may also derive from police or firedepartments, hospitals, and/or emergency responder communications;police reports; municipality information; automated Freedom ofInformation Act requests; and/or other data collected from governmentagencies and officials, which may be obtained from databases 18 forexample. The data from different sources or feeds may be aggregated.

The data generated may be transmitted, via wired or wirelesscommunication, to a server 14, which may comprise a remote server and/orother processor(s) associated with an insurance provider. The server 14and/or associated processors may build a database of thecustomer-related data, and/or otherwise store the data collected.

The server 14 and/or associated processors may analyze the datacollected and then perform certain actions and/or issue tailoredcommunications based upon the data, including the insurance-relatedactions or communications discussed elsewhere herein, and/or adjusting adynamic insurance product. The automatic gathering and collecting ofdata from several sources by the insurance provider, such as via wiredor wireless communication, may lead to expedited insurance-relatedactivity, including the automatic identification of insured eventsand/or changing insurance needs for the customer, and/or the automaticor semi-automatic processing or adjusting of insurance claims.

In one embodiment, customer and/or customer-related data (includinglocation and/or telematics data) may be collected by a mobile device(e.g., smart phone) application 34. An application 34 that collects thecustomer or customer-related data may ask an insured for permission tocollect and send customer data, customer-related data, and/or telematicsdata about driver behavior and/or vehicle usage to server 14 associatedwith an insurance provider. In return, the insurance provider mayprovide incentives to the insured, such as lower premiums or rates, ordiscounts. The application 34 for the mobile device 18 (and/or a smarthome controller or smart vehicle controller) may be downloadable off ofthe internet.

Exemplary Mobile Device or Vehicle Controller

Customer and/or customer-related data, including customer location data,may be collected via a customer mobile device, smart vehicle controller(or smart vehicle), and/or smart home controller. The mobile device,smart vehicle controller, and/or smart home controller may include aprocessor, wireless radio frequency transmitter and/or receiver, ortransceiver, clock, microphone and/or speaker, camera or video camera,sensor, memory, and/or power supply. The mobile device 18, smart vehiclecontroller, and/or smart home controller may include additional, fewer,or alternate components. Additionally or alternatively, the sensorsand/or sensors disbursed about a vehicle (or home) and/or affixed tovarious components therein may include similar functionality and/orcomponents as those of the mobile device, smart vehicle controller, orsmart home controller. The mobile device may include additional, less,or alternate functionality or components, including that discussedelsewhere herein.

The transceiver may be configured for wireless communication withsensors located about the vehicle (or home), other vehicles, othermobile devices, and/or remote servers and processors, such as thoselocated at an insurance provider location. The clock may be used totime-stamp the date and time that information is gathered or sensed byvarious sensors. For example, the clock may record the time and datethat photographs are taken by the camera, video is captured by thecamera, and/or other data is received by the mobile device, smartvehicle controller, and/or smart home controller.

The microphone and speaker may be configured for recognizing voice oraudio input and/or commands. The clock may record the time and date thatvarious sounds are collected by the microphone and speaker, such assounds of windows breaking, air bags deploying, tires skidding,conversations or voices of passengers, music within the vehicle, rain orwind noise, and/or other sound heard within or outside of a vehicle.

The sensor may be able to record audio or visual information. The sensormay alternatively be a speed, acceleration, directional, fluid, water,moisture, temperature, fire, smoke, wind, rain, snow, hail, motion,and/or other type of sensor, and/or gyro, compass, or accelerometer.

The memory may include software applications that control the mobiledevice, smart vehicle controller, smart home controller, and/or a mobiledevice, smart vehicle controller, or smart home controller displayscreen configured for accepting user input. The memory may includeinstructions for controlling or directing the operation of vehicleequipment that may prevent, detect, and/or mitigate vehicle damage. Thememory may further include instructions for controlling a smart vehicleor smart home wireless or wired network and interacting with mobiledevices and remote servers (and/or a remote server associated with aninsurance provider).

The power supply may be a battery or dedicated energy generator thatpowers the mobile device, smart vehicle controller, and/or smart homecontroller. The power supply may harvest energy from the vehicleenvironment and be partially or completely energy self-sufficient.

The smart vehicle controller may be affixed to the vehicle.Alternatively, the smart vehicle controller may be a mobile device (suchas a cellular telephone, smart phone, laptop, desktop computer, tablet,phablet, netbook, personal digital assistant (PDA), smart watch, smartglasses, wearable smart technology, pager, text messaging device, handheld communications device, and/or other device capable of one-way ortwo-way wireless communication), and/or other type of communicationsdevice.

The present embodiments may be implemented without changes or extensionsto existing communications standards. The smart vehicle controller orsmart home controller may also include or comprise a relay, node, accesspoint, Wi-Fi AP (Access Point), local node, pico-node, relay node,and/or a mobile device capable of RF (Radio Frequency) communication.The mobile device, smart home controller, and/or smart vehiclecontroller may include Wi-Fi, Bluetooth, GSM (Global System for Mobilecommunications), LTE (Long Term Evolution), CDMA (Code Division MultipleAccess), UMTS (Universal Mobile Telecommunications System), and/or othertypes of components and functionality.

Exemplary Computer-Implemented Methods

In one aspect, a computer-implemented method of providing and adjustinga dynamically reconfigurable insurance product covering multiple typesof insurance to an insured may be provided. The method may include, suchas with customer permission or affirmative consent, (1) receiving, at orvia one or more processors (such as a remote server or processorassociated with an insurance provider), customer-related data; (2)determining, at or via the one or more processors, a life event (orother customer activity), and/or type thereof, from computer analysis ofthe customer-related data, for example, by inputting thecustomer-related data (which may include mobile device, vehicle, orhome-mounted sensor data, and telematics data) into a machine learningprogram (and/or object recognition, facial recognition, or opticalcharacter recognition programs) to determine life events or othercustomer activity; (3) adjusting, at or via the one or more processors,the dynamically reconfigurable insurance product (and/or an associateddynamic insurance product premium or discount) based upon, at least inpart, the computer analysis of the customer-related data and/or lifeevent (or other customer activity), and/or type thereof, determined; (4)generating, at or via the one or more processors, a notification of theadjustment (or recommended adjustment) to the dynamically reconfigurableinsurance product, such as a wireless communication or data transmissionnotification; (5) transmitting, via the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), the notification to a mobile device or other computingdevice of the insured for the insured's review, approval, and/orrejection; (6) receiving, via or at the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), an approval or rejection of the adjustment (orrecommended adjustment) to the dynamically reconfigurable insuranceproduct from the mobile device or other computing device of the insured;and/or (7) adjusting or updating an insurance premium and/or discountassociated with the dynamically reconfigurable insurance product, at orvia the insurance provider remote server, to facilitate adjusting orotherwise providing a dynamic insurance product to the insured thatreflects current or changing insurance needs of the customer.

The dynamically reconfigurable insurance product may include auto, home,and life insurance. The customer-related data may include data collectedfrom a customer's mobile device, smart home controller, and/or smartvehicle controller. The customer-related data may include telematicsdata and/or information from social media.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or the machine learningprogram may include a marriage; a birth of child; a move to a newaddress; a death; a purchase or sale of a house or vehicle; and/or adivorce. The life event and/or customer activity determined fromcomputer analysis of the customer-related data may include adetermination that the customer is on a trip or vacation, and as aresult, coverages for auto and/or travel insurance are adjusted withinthe dynamically reconfigurable insurance product by the one or moreprocessors.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or the machine learningprogram may include a determination that the customer has purchased orsold a vehicle or home, and as a result, coverage for auto or homeinsurance, respectively, is adjusted within the dynamicallyreconfigurable insurance product by the one or more processors. The lifeevent and/or customer activity determined from computer analysis of thecustomer-related data may include a determination that the customer hasmarried or had a child (or is about to marry or have a child), and as aresult, coverage for life insurance is added or increased within thedynamically reconfigurable insurance product by the one or moreprocessors.

In another aspect, a computer-implemented method of providing andadjusting a dynamically reconfigurable insurance product coveringmultiple types of insurance to an insured may be provided. The methodmay include, first receiving customer permission or opt-in to a discountor rewards program, and then: (1) receiving, at or via one or moreprocessors (such as a remote server or processor associated with aninsurance provider), customer-related data; (2) determining, at or viathe one or more processors, a life event (or other customer activity),and/or type thereof, from computer analysis of the customer-relateddata, for example by inputting the customer-related data (which mayinclude mobile device, vehicle, or home-mounted sensor data, andtelematics data) into a machine learning program (and/or objectrecognition, facial recognition, or optical character recognitionprograms) to determine life events or other customer activity; (3)determining, at or via the one or more processors, (i) a new type ofinsurance to add to the dynamically reconfigurable insurance product,and/or (ii) a coverage amount for the new type of insurance based upon,at least in part, the computer analysis of the customer-related dataand/or the life event (or other customer activity), and/or type thereof,determined, or otherwise based upon the results of the machine learningprogram; (4) adjusting, at or via the one or more processors, a premiumfor the dynamically reconfigurable insurance product based upon, atleast in part, the new type of insurance to be added and/or the coverageamount of the new insurance type; (5) generating, at or via the one ormore processors, a notification of the adjustment (or recommendedadjustment) to the dynamically reconfigurable insurance product, such asa wireless communication or data transmission notification; (6)transmitting, via the one or more processors or associated transceiver(such as via wireless communication or data transmission), thenotification to a mobile device or other computing device of the insuredfor the insured's review, approval, and/or rejection; and/or (7)receiving, via or at the one or more processors or associatedtransceiver (such as via wireless communication or data transmission),an approval or rejection of the adjustment (or recommended adjustment)to the dynamically reconfigurable insurance product from the mobiledevice or other computing device of the insured to facilitate adding anew type of insurance to the dynamic insurance product to meet changinginsurance needs of the customer.

In another aspect, a computer-implemented method of providing andadjusting a dynamically reconfigurable insurance product coveringmultiple types of insurance to an insured may be provided. The methodmay include, first receiving customer permission or opt-in to a discountor rewards program, and then: (1) receiving, at or via one or moreprocessors (such as a remote server or processor associated with aninsurance provider), customer location data (such as GPS data from acustomer vehicle or mobile device); (2) determining, at or via the oneor more processors, a life event (or other customer activity), and/ortype thereof, from computer analysis of the customer location data, forexample by inputting the customer-related data (which may include mobiledevice, vehicle, or home-mounted sensor data, and telematics data) intoa machine learning program (and/or object recognition, facialrecognition, or optical character recognition programs) to determinelife events or other customer activity; (3) adjusting, at or via the oneor more processors, the dynamically reconfigurable insurance product(and/or an associated dynamic insurance product premium or discount)based upon, at least in part, the computer analysis of the customerlocation data and/or life event (or other customer activity), and/ortype thereof, determined, or based upon the results of the machinelearning technique; (4) generating, at or via the one or moreprocessors, a notification of the adjustment (or recommended adjustment)to the dynamically reconfigurable insurance product, such as a wirelesscommunication or data transmission notification; (5) transmitting, viathe one or more processors or associated transceiver (such as viawireless communication or data transmission), the notification to amobile device or other computing device of the insured for the insured'sreview, approval, and/or rejection; and/or (6) receiving, via or at theone or more processors or associated transceiver (such as via wirelesscommunication or data transmission), an approval or rejection of theadjustment (or recommended adjustment) to the dynamically reconfigurableinsurance product from the mobile device or other computing device ofthe insured to facilitate adjusting the dynamic insurance product tomeet changing insurance needs of the customer. The life event and/orcustomer activity determined from computer analysis of the customerlocation data may include a determination that the customer is on a tripor vacation, and auto and travel insurance are adjusted within thedynamically reconfigurable insurance product.

In another aspect, a computer-implemented method of providing andadjusting a dynamically reconfigurable insurance product coveringmultiple types of insurance to an insured may be provided. The methodmay include, first receiving customer permission or opt-in to a discountor rewards program, and then: (1) receiving, at or via one or moreprocessors (such as a remote server or processor associated with aninsurance provider), customer location data (such as GPS data from acustomer vehicle or mobile device); (2) determining, at or via the oneor more processors, customer activity, and/or type thereof (such as thecustomer is taking a trip or is on vacation), from computer analysis ofthe customer-related data, for example by inputting the customer-relateddata (which may include mobile device, vehicle, or home-mounted sensordata, and telematics data) into a machine learning program (and/orobject recognition, facial recognition, or optical character recognitionprograms) to determine life events or other customer activity; (3)determining, at or via the one or more processors, (i) a new type ofinsurance to add to the dynamically reconfigurable insurance product,and/or (ii) a coverage amount for the new type of insurance based upon,at least in part, the computer analysis of the customer location dataand/or the customer activity, and/or type thereof, determined, and/orthe results or output of the machine learning program; (4) adjusting, ator via the one or more processors, a premium for the dynamicallyreconfigurable insurance product based upon, at least in part, the newtype of insurance to be added and/or the coverage amount of the newinsurance type; (5) generating, at or via the one or more processors, anotification of the adjustment (or recommended adjustment) to thedynamically reconfigurable insurance product, such as a wirelesscommunication or data transmission notification; (6) transmitting, viathe one or more processors or associated transceiver (such as viawireless communication or data transmission), the notification to amobile device or other computing device of the insured for the insured'sreview, approval, and/or rejection; and/or (7) receiving, via or at theone or more processors or associated transceiver (such as via wirelesscommunication or data transmission), an approval or rejection of theadjustment (or recommended adjustment) to the dynamically reconfigurableinsurance product from the mobile device or other computing device ofthe insured to facilitate adding a new type of insurance to the dynamicinsurance product to meet changing insurance needs of the customer. Inone embodiment, the new type of insurance added to the dynamicallyreconfigurable insurance product may be travel insurance, and/or othertypes of insurance.

The foregoing methods may include additional, less, or alternateactions, including those discussed elsewhere herein, and/or may beimplemented via (i) one or more local or remote processors, such asprocessors associated with a customer mobile device, vehicle, or home,or insurance provider remote server, and/or (ii) computer-executableinstructions stored on non-transitory computer-readable media or medium.

Exemplary Computer Systems

In one aspect, a computer system configured for providing and adjustinga dynamically reconfigurable insurance product covering multiple typesof insurance to an insured may be provided. The system may include aprocessor (such as a remote server or processor associated with aninsurance provider) configured to, after receiving customer permissionor consent: (1) receive customer-related data and/or store thecustomer-related data received in a memory unit; (2) determine a lifeevent (or other customer activity), and/or type thereof, from computeranalysis of the customer-related data, for example by inputting thecustomer-related data (which may include mobile device, vehicle, orhome-mounted sensor data, and telematics data) into a machine learningprogram (and/or object recognition, facial recognition, or opticalcharacter recognition programs) to determine life events or othercustomer activity; (3) adjust the dynamically reconfigurable insuranceproduct (and/or an associated dynamic insurance product premium ordiscount) based upon, at least in part, the computer analysis of thecustomer-related data and/or life event (or other customer activity),and/or type thereof, determined, or the output of the machine learningor other program applied to the data; (4) generate a notification of theadjustment (or recommended adjustment) to the dynamically reconfigurableinsurance product, such as a wireless communication or data transmissionnotification; (5) transmit, or direct a transceiver to transmit (such asvia wireless communication or data transmission), the notification to amobile device or other computing device of the insured for the insured'sreview, approval, and/or rejection; (6) receive via the transceiver(such as via wireless communication or data transmission), an approvalor rejection of the adjustment (or recommended adjustment) to thedynamically reconfigurable insurance product from the mobile device orother computing device of the insured; and/or (7) adjust or update aninsurance premium and/or discount associated with the dynamicallyreconfigurable insurance product (and store the updated insurancepremium and/or discount in a memory unit for subsequent computer access)to facilitate adjusting or otherwise providing a dynamic insuranceproduct to the insured that reflects current or changing insurance needsof the customer.

The dynamically reconfigurable insurance product may include auto, home,and life insurance, as well as other types of insurance coverage. Thecustomer-related data may include data collected from a customer'smobile device, smart home controller, and/or smart vehicle controller.The customer-related data includes telematics data and/or informationfrom social media.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or machine learning techniquesmay include a marriage; a birth of child; a move to a new address; adeath; a purchase or sale of a house or vehicle; and/or a divorce. Thelife event and/or customer activity determined from computer analysis ofthe customer-related data may include a determination that the customeris on a trip or vacation, and coverages for auto and/or travel insuranceare adjusted within the dynamically reconfigurable insurance product bythe processor.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or machine learning techniquesmay include a determination that the customer has purchased or sold avehicle or home, and coverage for auto or home insurance, respectively,is adjusted within the dynamically reconfigurable insurance product bythe processor. The life event and/or customer activity determined fromcomputer analysis of the customer-related data may include adetermination that the customer has married or had a child (or is aboutto marry or have a child), and coverage for life insurance is added orincreased within the dynamically reconfigurable insurance product by theprocessor.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable insurance product covering multiple types ofinsurance to an insured may be provided. The system may include aprocessor (such as a remote server or processor associated with aninsurance provider) configured to, after receiving customer permission:(1) receiving customer-related data and/or store the customer-relateddata received in a memory unit; (2) determine a life event (or othercustomer activity), and/or type thereof, from computer analysis of thecustomer-related data, for example by inputting the customer-relateddata (which may include mobile device, vehicle, or home-mounted sensordata, and telematics data) into a machine learning program (and/orobject recognition, facial recognition, or optical character recognitionprograms) to determine life events or other customer activity; (3)determine (i) a new type of insurance to add to the dynamicallyreconfigurable insurance product, and/or (ii) a coverage amount for thenew type of insurance based upon, at least in part, the computeranalysis of the customer-related data and/or the life event (or othercustomer activity), and/or type thereof, determined, and/or results ofthe machine learning or other program applied to the data; (4) adjust apremium for the dynamically reconfigurable insurance product based upon,at least in part, the new type of insurance to be added and/or thecoverage amount of the new insurance type; (5) generate a notificationof the adjustment (or recommended adjustment) to the dynamicallyreconfigurable insurance product, such as a wireless communication ordata transmission notification; (6) transmit, or direct a transceiver totransmit (such as via wireless communication or data transmission), thenotification to a mobile device or other computing device of the insuredfor the insured's review, approval, and/or rejection; and/or (7) receivevia the transceiver (such as via wireless communication or datatransmission) an approval or rejection of the adjustment (or recommendedadjustment) to the dynamically reconfigurable insurance product from themobile device or other computing device of the insured (and/or store theupdated dynamically reconfigurable insurance product in a memory unitfor subsequent computer access and further updating over time) tofacilitate adding a new type of insurance to the dynamic insuranceproduct to meet changing insurance needs of the customer.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable insurance product covering multiple types ofinsurance to an insured may be provided. The system may include aprocessor (such as a remote server or processor associated with aninsurance provider) configured to, after receiving customer permission:(1) receive customer location data (such as GPS data from a customervehicle or mobile device) and store the customer location data in amemory unit; (2) determine a life event (or other customer activity),and/or type thereof, from computer analysis of the customer locationdata, for example by inputting the customer-related data (which mayinclude mobile device, vehicle, or home-mounted sensor data, andtelematics data) into a machine learning program (and/or objectrecognition, facial recognition, or optical character recognitionprograms) to determine life events or other customer activity; (3)adjust the dynamically reconfigurable insurance product (and/or anassociated dynamic insurance product premium or discount) based upon, atleast in part, the computer analysis of the customer location dataand/or life event (or other customer activity), and/or type thereof,determined, and/or output of the machine learning or otherprogram/technique applied to the data; (4) generate a notification ofthe adjustment (or recommended adjustment) to the dynamicallyreconfigurable insurance product, such as a wireless communication ordata transmission notification; (5) transmit via a transceiver (such asvia wireless communication or data transmission) the notification to amobile device or other computing device of the insured for the insured'sreview, approval, and/or rejection; and/or (6) receive via thetransceiver (such as via wireless communication or data transmission) anapproval or rejection of the adjustment (or recommended adjustment) tothe dynamically reconfigurable insurance product from the mobile deviceor other computing device of the insured (and storing the dynamicallyreconfigurable insurance product in a memory unit for subsequentcomputer access and further refinement or updating) to facilitateadjusting the dynamic insurance product to meet changing insurance needsof the customer. In one embodiment, the life event and/or customeractivity determined from computer analysis of the customer location datamay include a determination that the customer is on a trip or vacation,and auto and travel insurance are adjusted within the dynamicallyreconfigurable insurance product.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable insurance product covering multiple types ofinsurance to an insured may be provided. The system may include aprocessor (such as a remote server or processor associated with aninsurance provider) configured to, after receiving customer permission:(1) receive customer location data (such as GPS data from a customervehicle or mobile device) and stored the customer location data in amemory unit; (2) determine customer activity, and/or type thereof (suchas the customer is taking a trip or is on vacation), from computeranalysis of the customer-related data, for example by inputting thecustomer-related data (which may include mobile device, vehicle, orhome-mounted sensor data, and telematics data) into a machine learningprogram (and/or object recognition, facial recognition, or opticalcharacter recognition programs) to determine life events or othercustomer activity; (3) determine (i) a new type of insurance to add tothe dynamically reconfigurable insurance product, and/or (ii) a coverageamount for the new type of insurance based upon, at least in part, thecomputer analysis of the customer location data and/or the customeractivity, and/or type thereof, determined, and/or output of the machinelearning program; (4) adjust a premium for the dynamicallyreconfigurable insurance product based upon, at least in part, the newtype of insurance to be added and/or the coverage amount of the newinsurance type; (5) generate a notification of the adjustment (orrecommended adjustment) to the dynamically reconfigurable insuranceproduct, such as a wireless communication or data transmissionnotification; (6) transmit via a transceiver (such as via wirelesscommunication or data transmission) the notification to a mobile deviceor other computing device of the insured for the insured's review,approval, and/or rejection; and/or (7) receive via the transceiver (suchas via wireless communication or data transmission) an approval orrejection of the adjustment (or recommended adjustment) to thedynamically reconfigurable insurance product from the mobile device orother computing device of the insured (and storing the updateddynamically reconfigurable insurance product in a memory unit forsubsequent computer access and further refinement) to facilitate addinga new type of insurance to the dynamic insurance product to meetchanging insurance needs of the customer. In one embodiment, the newtype of insurance added to the dynamically reconfigurable insuranceproduct may be travel or other type insurance.

The foregoing computer systems may include processors, memory units,transceivers, displays, etc. The foregoing computer systems may includeadditional, less, or alternate functionality, including that discussedelsewhere herein.

Exemplary Dynamically Reconfigurable Financial Product

In one aspect, the dynamically reconfigurable product may beproduct-agnostic—able to handle all types (and/or combinations) ofproducts or services provided to customers that are more appropriate foreach customer based upon current life events or circumstances. Thus, inaddition to insurance products, the dynamically reconfigurable productor model may be applied to other types of products as well, such asfinancial products (including banking or loan products), home securityproducts, and products in other industries as well. One of the keybenefits for the customer is personalization of the adaptive or overallproduct, and one of the key benefits for the product provider isproviding more appropriate and changing products to the customer basedupon their current life circumstances.

The dynamically reconfigurable product discussed herein may includeseveral insurance and financial product or services that dynamicallyadjust or adapt over time to customer life events or life circumstances.For instance, the product may include a bundle of insurance andfinancial products, such as home, life, renters, and/or auto insurance,and one or more loans, mutual funds, stock accounts, saving or checkingaccounts, certificates of deposit, bonds, ETF's (electronically tradedfunds), etc.

As an example, if a customer has a child, a college savings account maybe added to the product or recommended to the customer. As anotherexample, if a customer gets married, a joint savings or checking accountmay be added to the product or recommended to the customer.

The dynamically reconfigurable product may be used to offer or adjustvarious financial or banking products, such as offering vehicle or homeloans, loan quotes, or loan refinancing. If the customer's credit scoreimproves, interest rates on vehicle loans, home loans, student loans, orcredit cards may be reduced, or associated recommendations sent to thecustomer for their review and approval. Or if the outstanding balance ona loan reaches a certain threshold, offers to re-finance may be sent tothe customer's mobile device, or interest rates on existing products maybe automatically reduced.

Additionally or alternatively, the dynamically reconfigurable productmay include offering various types of other financial products, such asmutual funds, savings or college savings accounts, and/or annuitiesbased upon life events or current customer life circumstances. As anexample, upon reaching a certain age, certain mutual funds or annuitiesmay be added to the product or otherwise recommended to the customer tohelp ensure that they have sufficient funds for retirement once theyreach retirement age.

Moreover, the dynamically reconfigurable product may include offeringupgrades to various types of smart home or smart vehicle (such asautonomous or semi-autonomous vehicle) technology that may mitigate riskto insured assets, such as insured homes or vehicles. Insurance costsavings or discounts may be offered to risk averse customers that employsuch risk mitigation technologies.

In one aspect, a computer-implemented method of providing and adjustinga dynamically reconfigurable financial product may be provided. Themethod may include: (1) receiving with customer permission, at or viaone or more processors (such as a remote server or processor associatedwith a financial services provider), customer-related data; (2)determining, at or via the one or more processors, a life event (orother customer activity), and/or type thereof, from computer analysis ofthe customer-related data, for example by inputting the customer-relateddata (which may include mobile device, vehicle, or home-mounted sensordata, and telematics data) into a trained machine learning program(and/or object recognition, facial recognition, or optical characterrecognition programs) to determine life events or other customeractivity; (3) adjusting, at or via the one or more processors, thedynamically reconfigurable financial product (and/or an associateddynamic financial product discount, cost, or interest rate) based upon,at least in part, the computer analysis of the customer-related dataand/or life event (or other customer activity), and/or type thereof,determined, and/or output of the trained machine learning program; (4)generating, at or via the one or more processors, a notification of theadjustment (or recommended adjustment) to the dynamically reconfigurablefinancial product, such as a wireless communication or data transmissionnotification; (5) transmitting, via the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), the notification to a mobile device or other computingdevice of the customer for the customer's review, approval, and/orrejection; (6) receiving, via or at the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), an approval or rejection of the adjustment (orrecommended adjustment) to the dynamically reconfigurable financialproduct from the mobile device or other computing device of thecustomer; and/or (7) adjusting or updating a discount, cost, or interestrate associated with the dynamically reconfigurable financial product,at or via the service provider remote server, to facilitate adjusting orotherwise providing a dynamic financial product to the customer thatreflects current or changing financial needs of the customer. The methodmay include additional, less, or alternate actions, including thosediscussed elsewhere herein, and/or may be implemented via one or morelocal or remote processors and/or via computer-executable instructionsstored on non-transitory computer-readable medium or media.

For instance, the dynamically reconfigurable financial product mayinclude a vehicle, home, or student loan, or vehicle, home, or studentloan re-financing or interest rate adjustment. The customer-related datamay include data collected from a customer's mobile device, smart homecontroller, smart vehicle controller, sensor data, and/or an onlinefinancial or credit account. The customer-related data may includetelematics data and/or information from social media.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or machine learning (or othertechniques applied to the data) may include a marriage; a birth ofchild; a move to a new address; a death; a purchase or sale of a houseor vehicle; a divorce; a change in income, a graduation from school,and/or other change in the customer's finances. The life event and/orcustomer activity determined from computer analysis of thecustomer-related data may include a determination that the customer'sfinancial situation has changed or about to change, such as a change inthe customer's income indicating that an additional financial product orservice may be appropriate for the customer.

Additionally or alternatively, the life event and/or customer activitydetermined from computer analysis of the customer-related data and/ormachine learning (or other techniques applied to the data) may include adetermination that the customer has purchased or sold a vehicle or home,and as a result, the customer's need for a vehicle or home loan haschanged, and a proposed change to the customer's vehicle or home loan(s)is adjusted within the dynamically reconfigurable financial product bythe one or more processors. Further, the life event and/or customeractivity determined from computer analysis of the customer-related dataand/or machine learning (or other techniques applied to the data) mayinclude a determination that the customer has married or had a child (oris about to marry or have a child), and as a result, additionalfinancial products or services may be appropriate, such as jointchecking or savings accounts, or a college savings account, andrecommended to be added within the dynamically reconfigurable financialproduct by the one or more processors. The dynamically reconfigurablefinancial product may include auto, home, and life insurance, and one ormore financial products (such as a vehicle loan, home loan, mutual fund,credit card account, debit card account, student loan, or checking orsavings account).

In another aspect, a computer-implemented method of providing andadjusting a dynamically reconfigurable financial product coveringmultiple types of financial services or products to a customer may beprovided. The method may include (1) receiving with customer permissionor consent, at or via one or more processors (such as a remote server orprocessor associated with an financial services or product provider),customer-related data; (2) determining, at or via the one or moreprocessors, a life event (or other customer activity), and/or typethereof, from computer analysis of the customer-related data, forexample by inputting the customer-related data (which may include mobiledevice, vehicle, or home-mounted sensor data, and telematics data) intoa trained machine learning program (and/or object recognition, facialrecognition, or optical character recognition programs) to determinelife events or other customer activity; (3) determining, at or via theone or more processors, (i) a new type of financial product or serviceto add to the dynamically reconfigurable financial product, and/or (ii)a size or financial amount of the new type of financial product basedupon, at least in part, the computer analysis of the customer-relateddata and/or the life event (or other customer activity), and/or typethereof, determined, and/or output of the machine learning or otherprogram; (4) adjusting, at or via the one or more processors, adiscount, cost, or interest rate associated with the dynamicallyreconfigurable financial product based upon, at least in part, the newtype of financial product or service to be added and/or the size offinancial amount of the new financial product or service; (5)generating, at or via the one or more processors, a notification of theadjustment (or recommended adjustment) to the dynamically reconfigurablefinancial product, such as a wireless communication or data transmissionnotification; (6) transmitting, via the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), the notification to a mobile device or other computingdevice of the insured for the insured's review, approval, and/orrejection; and/or (7) receiving, via or at the one or more processors orassociated transceiver (such as via wireless communication or datatransmission), an approval or rejection of the adjustment (orrecommended adjustment) to the dynamically reconfigurable financialproduct from the mobile device or other computing device of the insuredto facilitate adding a new type of financial product or service to thedynamically reconfigurable financial product to meet changing financialneeds of the customer. The dynamically reconfigurable financial productmay include auto, home, and life insurance, and one or more financialproducts (such as a vehicle loan, home loan, mutual fund, credit cardaccount, debit card account, student loan, or checking or savingsaccount). The method may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia one or more local or remote processors and/or viacomputer-executable instructions stored on non-transitorycomputer-readable medium or media.

Exemplary Computer-Defined Dynamic Product

FIG. 10 illustrates an exemplary method 1000 of adjusting acomputer-defined dynamic product based upon, at least in part, customeror customer-related data, and/or life event (and/or customer activity ortraveling) detection. The steps may be performed in the order shown inFIG. 10, or they may be performed in a different order. Furthermore,some steps may be performed concurrently as opposed to sequentially. Inaddition, some steps may be optional. The steps of thecomputer-implemented method 1000 may be performed by the system 10.

The method 1000 may include selling a computer-defined dynamic product1002; collecting and/or analyzing 1004 customer-related data at or viaserver 14; determining or detecting a life event (or customer activity)at or via the server 14 from computer analysis of the customer-relateddata 1004; automatically adjusting 1008 the computer-defined dynamicproduct, scope, and/or an associated payment schedule, discount, termscope, etc. at or via the server 14 based upon, at least in part, thetype of life event (or customer activity or traveling); generating andtransmitting 1010 a communication or notification regarding theadjustment/update (or even a proposed adjustment) to thecomputer-defined product to the customer mobile device 18 from theserver 14; causing the notification to be presented 1012 on thecustomer's mobile device 18 for the customer's review, approval, orrejection; and/or receiving 1014 a customer's approval or rejection ofthe changes, or recommended changes or coverages, from the customer'smobile device 18 at the server 14.

The method 1000 may include selling 1002 a computer-defined dynamicproduct. The computer-defined product may originally include one or moretypes of insurance coverage. Over time, other types of insurancecoverage may be added or dropped. Also over time, coverages, limits, ordeductibles associated with the different types of insurance may beadjusted, such as discussed elsewhere herein.

However, in one aspect, the dynamically reconfigurable product may beproduct-agnostic—able to handle all types (and/or combinations) ofproducts or services provided to customers that are more appropriate foreach customer based upon current life events or circumstances. Thus, inaddition to insurance products, the dynamically reconfigurablecomputer-defined product or model may be applied to other types ofproducts as well, such as financial products (including banking or loanproducts), home security products, and products in other industries aswell. One of the key benefits for the customer is personalization of theadaptive or overall product, and one of the key benefits for the productprovider is providing more appropriate and changing products to thecustomer based upon their current life circumstances.

The dynamically reconfigurable product discussed herein may includeseveral insurance and financial product or services that dynamicallyadjust or adapt over time to customer life events or life circumstances.For instance, the product may include a bundle of insurance andfinancial products, such as home, life, renters, and/or auto insurance,and one or more loans, mutual funds, stock accounts, saving or checkingaccounts, certificates of deposit, bonds, ETF's (electronically tradedfunds), etc.

As an example, if a customer has a child, a college savings account maybe added to the product or recommended to the customer. As anotherexample, if a customer gets married, a joint savings or checking accountmay be added to the product or recommended to the customer.

The dynamically reconfigurable computer-defined product may be used tooffer or adjust various financial or banking products, such as offeringvehicle or home loans, loan quotes, or loan refinancing. If thecustomer's credit score improves, interest rates on vehicle loans, homeloans, student loans, or credit cards may be reduced, or associatedrecommendations sent to the customer for their review and approval. Orif the outstanding balance on a loan reaches a certain threshold, offersto re-finance may be sent to the customer's mobile device, or interestrates on existing products may be automatically reduced.

Additionally or alternatively, the dynamically reconfigurable productmay include offering various types of other financial products, such asmutual funds, savings or college savings accounts, and/or annuitiesbased upon life events or current customer life circumstances. As anexample, upon reaching a certain age, certain mutual funds or annuitiesmay be added to the product or otherwise recommended to the customer tohelp ensure that they have sufficient funds for retirement once theyreach retirement age.

Moreover, the dynamically reconfigurable product may include offeringupgrades to various types of smart home or smart vehicle (such asautonomous or semi-autonomous vehicle) technology that may mitigate riskto insured assets, such as insured homes or vehicles. Insurance costsavings or discounts may be offered to risk averse customers that employsuch risk mitigation technologies.

The method 1000 may include collecting and/or analyzing 1004 customer orcustomer-related data at a server 14, such as with customer permission.Customer or customer-related data may be generated from various types ofsensors, including those associated with the customer's mobile device(s)18, home, vehicle, and/or home computer. The data may be transmitted toan insurance provider server 14 or processor via wired or wirelesscommunication and/or data transmission from a transceiver associatedwith the customer's mobile device 18, home (such as a smart homecontroller), vehicle (such as a smart vehicle controller), and/or homecomputer.

The method 1000 may include determining or detecting 1006 a life event(and/or other customer activity) at the server 14 from computer analysisof the customer or customer-related data with customer permission, forexample by inputting the customer-related data (which may include mobiledevice, vehicle, or home-mounted sensor data, and telematics data) intoa trained machine learning program (and/or object recognition, facialrecognition, or optical character recognition programs) to determinelife events or other customer activity. For instance, from analysis ofthe data, it may be determined that the insured is buying or has boughta vehicle or home, is getting married, is expecting a child, is moving,is planning a trip or vacation, and/or about to experience, or hasexperienced, an event that has changed their insurance needs.

The life event and/or customer activity determined from computeranalysis of the customer-related data and/or machine learning mayinclude a marriage; a birth of child; a move to a new address; a death;a purchase or sale of a house or vehicle; a divorce; a change in income,a graduation from school, and/or other change in the customer'sfinances. The life event and/or customer activity determined fromcomputer analysis of the customer-related data may include adetermination that the customer's financial situation has changed orabout to change, such as a change in the customer's income indicatingthat an additional financial product or service may be appropriate forthe customer.

Additionally or alternatively, the life event and/or customer activitydetermined from computer analysis of the customer-related data and/ormachine learning may include a determination that the customer haspurchased or sold a vehicle or home, and as a result, the customer'sneed for a vehicle or home loan has changed, and a proposed change tothe customer's vehicle or home loan(s) is adjusted within thedynamically reconfigurable financial product by the one or moreprocessors. Further, the life event and/or customer activity determinedfrom computer analysis of the customer-related data and/or machinelearning may include a determination that the customer has married orhad a child (or is about to marry or have a child), and as a result,additional financial products or services may be appropriate, such asjoint checking or savings accounts, or a college savings account, andrecommended to be added within the dynamically reconfigurable financialproduct by the one or more processors. The dynamically reconfigurablefinancial product may include auto, home, and life insurance, and one ormore financial products (such as a vehicle loan, home loan, mutual fund,credit card account, debit card account, student loan, or checking orsavings account).

The method 1000 may include automatically adjusting 1008 thecomputer-defined dynamic product and/or an associated insurance premium,discount, coverage, deductible, limit, etc. at the remote server basedupon, at least in part, the type of life event (or other activity)detected. For instance, when a home is purchased, the computer-definedproduct may automatically be adjusted to include an appropriate amountof homeowners insurance for the insured, or if a new vehicle ispurchased, that vehicle may be automatically insured at a recommended orappropriate level. The adjustments to the computer-defined dynamicproduct may be permanent and immediately binding and legallyenforceable. Alternatively, the adjustments to the computer-defineddynamic product may be temporary, such as dependent upon customer reviewand approval.

For instance, various types of insurance (auto, home, renters, life,travel, etc.) may be adjusted in real-time or near real-time based uponGPS information received from the customer's mobile device 18 or othercomputing device, such as discussed elsewhere herein. Other adjustmentsmay be made based upon customer or customer device information.

The computer-defined product may also be adjusted 1008 based upon, atleast in part, customer location data, such as GPS data received from acustomer mobile device 18 or vehicle. As an example, if it is determinedthat the customer is taking a trip and not using their personal vehicle,their auto insurance rate may be dynamically adjusted, and/or thecomputer-defined product may be automatically adjusted to include travelinsurance. As another example, it may be determined that ageographically-sensitive computer-defined product—such as a loan forin-state tuition or home security and monitoring—may be affected bylong-term or short-term location status changes, thereby promptingappropriate notifications and/or updates/changes to the computer-definedproduct. Other adjustments to the computer-defined product may also bemade, including those discussed elsewhere herein.

The method 1000 may include generating and transmitting 1012 acommunication or notification regarding the adjustment (or proposedadjustment) to the computer-defined product to the customer's mobiledevice 18 from the server 14. Server 14 may generate a notification ofchanges to the computer-defined product, and/or proposed changes to thecomputer-defined product. The server 14 may then transmit thenotification, via wireless communication or data transmission, to theinsured's mobile device 18, vehicle, home, or other computing device.

The method 1000 may include causing 1014 the notification to bepresented on the customer's mobile device 18 for the customer's review,approval, or rejection. The notification regarding the changes orproposed changes, or other insurance recommendations, may be presentedon a mobile device 18, computing device, vehicle, or home display screenfor the customer's approval or rejection, for example as instructed bysoftware application 34.

The method 1000 may include receiving 1016 a customer's approval orrejection of the changes, or recommended changes or coverages from thecustomer's mobile device 18 at the server 14. The recommendations mayinclude adding or removing certain aspects of the computer-definedproduct. Once the customer's approval or rejection of the changes orrecommended changes to the computer-defined product are received at theserver 14, such as via wireless communication or data transmission froma customer's mobile device 18, vehicle, home, or other computing device,the server 14 may update the computer-defined product accordingly.

The method 1000 may include additional, less, or alternate actions,including those discussed elsewhere herein, and/or may be implementedvia computer system 10, communication network 12, one or more otherprocessors or servers (e.g., other vehicle control/communicationsystems, mobile devices, and/or remote servers), and/or othercomputer-executable instructions stored on non-transitory storage mediaor computer readable medium.

Exemplary Machine Learning Embodiments

In one aspect, a computer-implemented method for determining anindividual's overall risk profile and calculating or generating adynamically reconfigurable insurance product may be provided. Thecomputer-implemented method may include (1) receiving via a radio link,at one or more processors and/or transceivers, sensor data that wasgenerated by one or more sensors positioned in and around a house (andtelematics data generated by one or more vehicle or mobile devicesensors); (2) generating, via the one or more processors, a data filethat includes the sensor data (and telematics data) received; (3)receiving or retrieving current product data, via the one or moreprocessors, that is derived from an existing dynamically reconfigurableinsurance product, the current product data being stored in a memoryunit; (4) inputting, via the one or more processors, (a) the sensor data(and/or telematics data); and (b) the current product data from theexisting dynamically reconfigurable insurance product into a machinelearning program to generate a resident profile that includes anindicator of an overall level of risk and/or type of risks for anindividual or household; and/or (5) calculating or generating, via theone or more processors, an updated dynamically reconfigurable insuranceproduct based upon the indicator of an overall level of risk and/or typeof risks for an individual or household.

The method may include transmitting over a radio link, via the one ormore processors, the updated dynamically reconfigurable insuranceproduct to a mobile device of the individual for their review, approval,or modification. The machine learning program analyzes the data inputtedto determine one or more home features, and one or more vehicle featuresand adjusts the overall level of risk and type of risks for theindividual or household based upon the home or vehicle featuresdetermined. The machine learning program analyzes the data inputted todetermine number of residents in the household, and number and types ofpets in the household, and adjusts the overall level of risk and type ofrisks for the individual or household based upon the number of residentsand number/type of pets. The method may include one or more processorsgenerating a report that includes a listing of a plurality of eventsrecorded by each sensor or a vehicle/mobile device. The method mayinclude utilizing the machine learning program to determine patterns ofactivity from individuals within the house. The method may includeutilizing the machine learning program to associate patterns of activitywith particular individuals within the house.

In another aspect, a computer-implemented method for determining anindividual's overall risk profile and calculating or generating adynamically reconfigurable insurance product may be provided. Thecomputer-implemented method may include (1) receiving via a radio link,at one or more processors and/or transceivers, (vehicle-mounted, mobiledevice, and/or home-mounted) sensor data that was generated by one ormore sensors positioned in and around a house, vehicle, or mobile device(and telematics data generated by one or more vehicle or mobile devicesensors); (2) generating, via the one or more processors, a data filethat includes the sensor data (and telematics data) received; (3)receiving or retrieving current product data, via the one or moreprocessors, that is derived from an existing dynamically reconfigurableinsurance product, the current product data being stored in a memoryunit; (4) inputting, via the one or more processors, (a) the sensor data(and/or telematics data); and (b) the current product data from theexisting dynamically reconfigurable insurance product into a machinelearning program to generate a resident profile that includes anindicator of an overall level of risk, type of risks for an individualor household, and recommended changes to insurance coverage; and/or (5)calculating or generating, via the one or more processors, an updateddynamically reconfigurable insurance product based upon the indicator ofan overall level of risk and/or type of risks for an individual orhousehold.

The methods may include additional, less, or alternate actions,including those discussed elsewhere herein. The methods may beimplemented via one or more local or remote processors and transceivers,and/or via computer-executable instructions stored on computer-readablemedia or medium.

Exemplary Computer Systems

In one aspect, a computer system configured to adjust a dynamicallyreconfigurable product of a customer may be provided. The system mayinclude a processor configured to: (1) receive, at or via at least onetransceiver over a wireless communication channel, customer-relateddata; (2) determine customer activity from computer analysis of thecustomer-related data; (3) generate an updated dynamicallyreconfigurable product based upon the computer analysis of thecustomer-related data; (4) generate an electronic notification of theupdated dynamically reconfigurable product; (5) transmit, at or via theat least one transceiver over the wireless communication channel, theelectronic notification to a computing device of the customer; and/or(6) receive, at or via the at least one transceiver, a response to theupdated dynamically reconfigurable product from the computing device tofacilitate meeting a customer's changing circumstances. The computersystem may include additional, less, or alternate functionality,including the functionality mentioned elsewhere herein.

The computer analysis comprises inputting the customer-related data intoa trained machine learning program executed by the processor, thetrained machine learning program being trained to determine customeractivity or a type of customer activity from the customer-related data.The processor may be further configured to generate a customer profilethat includes (i) at least one risk type, or (ii) an overall level ofrisk, and the updated dynamically reconfigurable product is generatedbased, at least in part, on the (i) at least one risk type, or (ii)overall level of risk. The processor may be further configured toreceive current product data, and the computer analysis may includeinputting the current product data into the trained machine learningprogram.

The customer-related data may indicate one or more features of a home,and the (i) at least one risk type, or (ii) overall level of risk isgenerated, at least in part, based upon the one or more features of thehome; and the dynamically reconfigurable product may include homeownersinsurance. The customer-related data may indicate one or more featuresof a vehicle, and the (i) at least one risk type, or (ii) overall levelof risk is generated, at least in part, based upon the one or morefeatures of the vehicle; and the dynamically reconfigurable product mayinclude auto insurance. The customer activity may indicate a number ofresidents in a residence, and the (i) at least one risk type, or (ii)overall level of risk is generated, at least in part, based upon thenumber of residents; and the dynamically reconfigurable product mayinclude at least one of homeowners insurance and renters insurance.

Additionally or alternatively, the customer profile may include at leastone pattern of activity associated with at least one individual, and the(i) at least one risk type, or (ii) overall level of risk is based, atleast in part, on the at least one pattern of activity. Thecustomer-related data may include personal and/or health-relatedinformation relevant to an underwriting process, and the (i) at leastone risk type, or (ii) overall level of risk is generated, at least inpart, based upon the computer analysis of the personal and/orhealth-related information; the dynamically reconfigurable product mayinclude at least one of health insurance and life insurance; and thepersonal and/or health related information may include at least one of:(i) an image, (ii) a picture, (iii) a video, and (iv) an audiorecording, and the processor may be further configured to apply at leastone of an object recognition program, facial recognition program and anoptical character recognition program to the personal and/or healthrelated information and apply or input the result into the trainedmachine learning program to determine or adjust the (i) at least onerisk type, or (ii) overall level of risk.

A rejection of the updated dynamically reconfigurable product may bereceived with a recommended change, in the response from the computingdevice. The processor may be further configured to apply at least one ofan object recognition program, facial recognition program and an opticalcharacter recognition program to the customer-related data and input theresult into the trained machine learning program, the customer-relateddata including sensor data collected from at least one of a home-mountedsensor, a vehicle-mounted sensor, and a customer mobile device sensor.

The processor may be further configured to generate recommended changesto the dynamically reconfigurable product, the recommended changesincluding at least one of (i) a new type of insurance to add to thedynamically reconfigurable product, and (ii) a coverage amount for thenew type of insurance, and the processor may be further configured toadjust a premium for the dynamically reconfigurable product based uponthe new type of insurance to add and the coverage amount of the new typeof insurance. The customer-related data may include sensor datacollected from at least one of (i) a mobile device, (ii) a smart homecontroller, and (iii) a smart vehicle controller; and the customeractivity may include, or be associated with, a determination that thecustomer is on a trip or vacation, and the updated dynamicallyreconfigurable product may include (a) an increased auto insurancepremium, (b) new or additional travel insurance, and/or (c) an adjustedpremium for homeowners insurance or renters insurance.

The processor may be further configured to: prior to generating theupdated dynamically reconfigurable product, transmit, at or via the atleast one transceiver over one or more radio links or wirelesscommunication channels, a prompt to the customer inquiring about alength of the trip or vacation; and receive, at or via the at least onetransceiver over one or more radio links or wireless communicationchannels, a response from the computing device indicating the length ofthe trip or vacation.

The customer activity may include, or be associated with, (i) adetermination that the customer has purchased a vehicle, and the updateddynamically reconfigurable product may include additional autoinsurance; or (ii) a determination that the customer has purchased ahome, and the updated dynamically reconfigurable product may include newor additional homeowners insurance.

The customer activity may include (i) a determination that the customerhas married or had a child, and the updated dynamically reconfigurableproduct may include additional life insurance; or (ii) a determinationthat the customer has moved into an apartment, and the updateddynamically reconfigurable product may include new or additional rentersinsurance.

The customer activity may include a determination that the customer'sfinances have undergone a significant change, and the updateddynamically reconfigurable product may include a new or an additionalfinancial product, the new or additional financial product being avehicle loan or a home loan.

The customer activity may include a determination that the customerengages in an identified average daily commute, and the updateddynamically reconfigurable product may include an increased autoinsurance premium. The dynamically reconfigurable insurance product mayinclude at least one of auto insurance, homeowners insurance, lifeinsurance, commercial insurance, vehicle loan, home loan, mutual fund,credit card account, debit card account, student loan, checking accountand savings account. The updated dynamically reconfigurable product mayinclude at least one of a new or an additional vehicle loan, home loan,and/or student loan; an adjusted vehicle loan interest rate, home loaninterest rate, and/or student loan interest rate; and/or a re-financedstudent or home loan.

The customer activity determined from computer analysis of thecustomer-related data may include at least one of a marriage, a birth ofchild, a move to a new address, a death, a purchase of a house, a saleof a house, a purchase of a vehicle, a sale of a vehicle, a divorce, achange in income, a graduation from school, a significant change in thecustomer's finances or the customer's business or business personalproperty.

The customer-related data may include data automatically obtained from athird party data source with customer consent or permission, the thirdparty source being chosen from a group consisting of: a bureau of motorvehicles database, a court records database, a national governmentalrecords database, a state governmental records database, a countygovernmental records database, a local municipality records database, agovernment agency records database, a utility provider records database,a cable company records database, and a phone company records database.

In another aspect, a computer system configured to adjust a dynamicallyreconfigurable product of a customer may be provided. The system mayinclude a processor configured to: (1) receive, at or via at least onetransceiver over a wireless communication channel, customer-relateddata; (2) receive current product data derived from the dynamicallyreconfigurable product, the current product data being stored in amemory unit; (3) generate a customer profile from computer analysis ofthe customer-related data and the current product data, the customerprofile including at least one type of risk; (4) generate an updateddynamically reconfigurable product based, at least in part, upon thecustomer profile; (5) generate an electronic notification (capable ofwireless communication or data transmission over one or more radio linksor wireless communication channels) of the updated dynamicallyreconfigurable product; (6) transmit, at or via the at least onetransceiver over the wireless communication channel, the notification toa computing device of the customer; and/or (7) receive, at or via the atleast one transceiver over the wireless communication channel, anelectronic response to the updated dynamically reconfigurable productfrom the computing device. The computer system may include additional,less, or alternate functionality, including that discussed elsewhereherein.

For instance, the computer analysis may include inputting thecustomer-related data and the current product data into a trainedmachine learning program. The customer profile may include an overalllevel of risk.

The customer-related data and the current product data may indicate oneor more features of a home; and the overall level of risk may begenerated, at least in part, based upon the one or more features of thehome, the dynamically reconfigurable product including homeownersinsurance. Additionally or alternatively, the customer-related data andthe current product data may indicate one or more features of a vehicle,and the overall level of risk may be generated, at least in part, basedupon the one or more features of the vehicle, the dynamicallyreconfigurable product including auto insurance.

The customer-related data and the current product data may indicate anumber of residents in a residence, and the overall level of risk may begenerated, at least in part, based upon the number of residents, thedynamically reconfigurable product including at least one of homeownersinsurance and renters insurance. The customer-related data and thecurrent product data may indicate one or more features orcharacteristics of a business; and the overall level of risk may begenerated, at least in part, based upon the one or more features orcharacteristics of the business, the dynamically reconfigurable productincluding commercial insurance.

The customer profile may include at least one pattern of activityassociated with at least one individual or business entity, and whereinthe at least one risk type is based, at least in part, on the at leastone pattern of activity.

The customer-related data and current product data may include personaland/or health-related information relevant to an underwriting process,and the overall level of risk may be generated, at least in part, basedupon the computer analysis of the personal and/or health-relatedinformation, the dynamically reconfigurable product including at leastone of health insurance and life insurance. The personal and/or healthrelated information may include at least one of: (i) an image, (ii) apicture, (iii) a video, and (iv) an audio recording, and the processormay be further configured to apply at least one of an object recognitionprogram, facial recognition program and an optical character recognitionprogram to the personal and/or health related information and input theresult into the trained machine learning program.

The updated dynamically reconfigurable product may include at least oneof (i) a new type of insurance to add to the dynamically reconfigurableproduct, and (ii) a coverage amount for the new type of insurance, andthe processor is further configured to adjust a premium for thedynamically reconfigurable product based upon the new type of insuranceand the coverage amount for the new type of insurance.

The customer-related data may include sensor data collected from atleast one of (i) a mobile device, (ii) a smart home controller, and(iii) a smart vehicle controller. At least some of the sensor data maybe collected from the smart home controller and the dynamicallyreconfigurable product may include homeowners insurance or rentersinsurance. The customer-related data may indicate that the customer ison a trip or vacation. The updated dynamically reconfigurable productmay include an increased or decreased auto, life, or home insurancepremium, and/or new or additional travel insurance.

The processor may be further configured to: prior to generating theupdated dynamically reconfigurable product, transmit, at or via the atleast one transceiver over the wireless communication channel or radiolink, a prompt to the customer inquiring about a length of the trip orvacation; and/or receive, at or via the at least one transceiver overthe wireless communication channel or radio link, a response from thecomputing device indicating the length of the trip or vacation.

The processor may be further configured to, prior to determiningcustomer activity from computer analysis, train the trained machinelearning program by inputting a sample data set and executing thetrained machine learning program, the sample data set including at leastone of: (i) an image, (ii) a picture, (iii) a video, and (iv) an audiorecording. The processor may be further configured to, prior toinputting the sample data set, apply at least one of an objectrecognition program, facial recognition program and an optical characterrecognition program to the sample data set.

The updated dynamically reconfigurable product may include an increasedor decreased health or life insurance premium. The customer-related datamay indicate that the customer's finances have undergone a significantchange, and the updated dynamically reconfigurable product may include anew or an additional financial product. The significant change mayresult from marriage or a child, and the new or additional financialproduct may be a joint financial account, a new savings account, a newcollege savings account, or a new whole or variable life insuranceproduct.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable product covering multiple types of insurancemay be provided. The system may include one or more processors andtransceivers configured to: (1) receive customer-related data and sensordata over one or more radio links and store the customer-related dataand sensor data received in a memory unit; (2) input thecustomer-related data and sensor data into a machine learning program,the machine learning program being trained to determine life events fromthe customer-related data and sensor data; (3) determine (i) a new typeof insurance to add to the dynamically reconfigurable insurance product,and (ii) a coverage amount for the new type of insurance based upon, atleast in part, a life event identified; (4) adjust a premium for thedynamically reconfigurable insurance product based upon, at least inpart, the new type of insurance to be added and the coverage amount ofthe new insurance type; (5) generate an electronic notification of therecommended adjustment to the dynamically reconfigurable insuranceproduct; (6) transmit over a wireless communication channel theelectronic notification to a mobile device or other computing device ofthe insured for the insured's review; and/or (7) receive over thewireless communication channel an electronic approval or rejection ofthe recommended adjustment to the dynamically reconfigurable productfrom the mobile device or other computing device of the insured tofacilitate adding a new type of insurance to the dynamic product to meetchanging insurance needs or circumstances of the customer. The computersystem may include additional, less, or alternate functionality,including that discussed elsewhere herein.

For instance, the dynamically reconfigurable product may include auto,home, and life insurance, and one or more loan products. Thecustomer-related data may include data collected from a customer'smobile device, smart home controller, or smart vehicle controller. Thecustomer-related data may include telematics data or information fromsocial media.

The life event may include, or be, a marriage; a birth of child; a moveto a new address; a death; a purchase or sale of a house or vehicle; adivorce, or a purchase of a new business. The life event may include, orbe associated with, a determination that the customer is on a trip orvacation, and the new type of insurance added to the dynamicallyreconfigurable product may be travel insurance.

The life event may include, or be associated with, a determination thatthe customer has purchased a vehicle, and the new type of insuranceadded to the dynamically reconfigurable product may be auto insurance.The life event may include, or be associated with, a determination thatthe customer has purchased a house, and the new type of insurance addedto the dynamically reconfigurable product may be homeowners insurance.The life event may include, or be associated with, a determination thatthe customer has moved into an apartment, and the new type of insuranceadded to the dynamically reconfigurable product may be rentersinsurance. The life event may include, or be associated with, adetermination that the customer has married or had a child, and the newtype of insurance added to the dynamically reconfigurable product may belife insurance. The life event may include, or be associated with, adetermination that the customer has had a child, and the new type ofinsurance added to the dynamically reconfigurable product may be asavings account or college savings account. The life event may include,or be associated with, a determination that the customer has added a newpet to the household, and the new type of insurance added to thedynamically reconfigurable product may be pet insurance.

The sensor data may include data collected from mobile device-mountedsensors, home-mounted sensors, vehicle-mounted sensors. The sensor datamay include data collected from mobile device mounted sensors,home-mounted sensors, vehicle-mounted sensors, and the customer-relateddata may include data or information gathered from an internet or otherwireless communication network.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable product covering multiple types of insurancemay be provided. The system may include one or more processors andtransceivers configured to: (1) receive customer GPS location data andsensor data over one or more radio links, and store the customer GPSlocation data and sensor data in a memory unit; (2) input the customerGPS location data and sensor data into a machine learning program totrained to determine customer activity, or type thereof computeranalysis of the customer GPS location data and sensor data; (3)determine (i) a new type of insurance to add to the dynamicallyreconfigurable product, and/or (ii) a coverage amount for the new typeof insurance based upon, at least in part, the customer activity, ortype thereof, determined; (4) adjust a premium for the dynamicallyreconfigurable product based upon, at least in part, the new type ofinsurance to be added and the coverage amount of the new insurance type;(5) generate an electronic notification of the recommended adjustment tothe dynamically reconfigurable product; (6) transmit over one or moreradio links the electronic notification to a mobile device or othercomputing device of the customer for the customer's review; and/or (7)receive an electronic approval or rejection of the recommendedadjustment to the dynamically reconfigurable product from the mobiledevice or other computing device of the customer to facilitate adding anew type of insurance to the dynamic reconfigurable product to meetchanging needs of the customer.

The system may include additional, less, or alternate actions, includingthose discussed elsewhere herein. For instance, the customer activitydetermined from the trained machine learning program may be that thecustomer is on a trip or vacation, and the new type of insurance addedto the dynamically reconfigurable product may be travel insurance. Thecustomer activity determined from the trained machine learning programmay be that the customer is on a trip or vacation, and a premium forexisting auto insurance for the customer may be reduced, or otherwiseadjusted, to reflect non-usage of an insured vehicle.

In another aspect, a computer system configured to provide and adjust adynamically reconfigurable product covering multiple types of insurancemay be provided. The system may include one or more processors andtransceivers configured to: (1) receive customer-related (and/orbusiness-related) data and sensor data over one or more radio links andstore the customer-related (and/or business-related) data and sensordata received in a memory unit; (2) input the customer-related (and/orbusiness-related) data and sensor data into a machine learning program,the machine learning program being trained to determine customer (and/orbusiness) activity from the customer-related (and/or business-related)data and sensor data; (3) determine (i) a new type of insurance to addto the dynamically reconfigurable insurance product, and (ii) a coverageamount for the new type of insurance based upon, at least in part, thecustomer (and/or business) activity identified; (4) adjust a premium forthe dynamically reconfigurable insurance product based upon, at least inpart, the new type of insurance to be added and the coverage amount ofthe new insurance type; (5) generate an electronic notification of therecommended adjustment to the dynamically reconfigurable insuranceproduct; (6) transmit over a wireless communication channel theelectronic notification to a mobile device or other computing device ofthe insured for the insured's review; and/or (7) receive over thewireless communication channel an electronic approval or rejection ofthe recommended adjustment to the dynamically reconfigurable productfrom the mobile device or other computing device of the insured tofacilitate adding a new type of insurance to the dynamic product to meetchanging insurance needs or circumstances of the customer and/orbusinesses. The computer system may include additional, less, oralternate functionality, including that discussed elsewhere herein.

In some embodiments, the customer activity determined from the trainedmachine learning program may be related to life events. Additionally oralternatively, the customer activity determined from the trained machinelearning program may be related to a business, or include businessactivity. If the customer activity is business-related, a level ofcommercial property insurance coverage may be adjusted to reflectadditional or less business or personal property; a level of businessincome insurance coverage may be adjusted to reflect increased ordecreased business income; a level of crime or equipment breakdowninsurance coverage may be adjusted to reflect increased or decreasedrisk; a level of inland marine or ocean marine insurance coverage may beadjusted to reflect increased or decreased risk; a level of workerscompensation insurance coverage may be adjusted to reflect increased ordecreased risk; a level of commercial general liability insurancecoverage may be adjusted to reflect increased or decreased risk; a levelof business or farm insurance coverage may be adjusted to reflectincreased or decreased risk; and/or a level of commercial auto insurancecoverage may be adjusted to reflect increased or decreased risk, oradditional or fewer vehicles owned by the business. ADDITIONALCONSIDERATIONS

With the foregoing, an insurance customer may opt into a rewards,insurance discount, or other type of program. After the insurancecustomer provides their affirmative consent, an insurance providerremote server may collect image data of insured assets or life eventsfrom the insurance customer's home computer, mobile device, smartvehicle, smart home, etc. For instance, data may be collected thatindicates a life event, such as the purchase of new home or vehicle,birth, marriage, move, etc., such as with the insured's permission. Withrespect to purchasing a new home or vehicle, the data may indicatevehicle or home features, including safety or risk mitigation features.In return, risk averse drivers and/or vehicle owners (such as owners orautonomous or semi-autonomous vehicles with safety features ortechnology) may receive insurance discounts and/or be provided moreappropriate levels of insurance coverages.

In one aspect, mobile device, smart home, or smart vehicle data, and/orthe other types of data discussed elsewhere herein, may be collected orreceived by an insurance provider remote server, such as via direct orindirect wireless communication or data transmission from the customer'scomputing device, after a customer affirmatively consents or otherwiseopts into an insurance discount, reward, or other program. The insuranceprovider may then analyze the data received with the customer'spermission to provide benefits to the customer. As a result, risk aversecustomers may receive insurance discounts or other insurance costsavings based upon data that reflects low risk behavior and/ortechnology that mitigates or prevents risk to (i) insured assets, suchas vehicles or homes, and/or (ii) vehicle operators or passengers, orhome occupants.

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereferences to “one embodiment”, “an embodiment”, or “embodiments” inthis description do not necessarily refer to the same embodiment and arealso not mutually exclusive unless so stated and/or except as will bereadily apparent to those skilled in the art from the description. Forexample, a feature, structure, act, etc. described in one embodiment mayalso be included in other embodiments, but is not necessarily included.Thus, the current technology can include a variety of combinationsand/or integrations of the embodiments described herein.

Although the present application sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof routines, subroutines, applications, or instructions. These mayconstitute either software (e.g., code embodied on a machine-readablemedium or in a transmission signal) or hardware. In hardware, theroutines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) ascomputer hardware that operates to perform certain operations asdescribed herein.

In various embodiments, computer hardware, such as a processing element,may be implemented as special purpose or as general purpose. Forexample, the processing element may comprise dedicated circuitry orlogic that is permanently configured, such as an application-specificintegrated circuit (ASIC), or indefinitely configured, such as an FPGA,to perform certain operations. The processing element may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement the processingelement as special purpose, in dedicated and permanently configuredcircuitry, or as general purpose (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “processing element” or equivalents should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which the processing element is temporarily configured(e.g., programmed), each of the processing elements need not beconfigured or instantiated at any one instance in time. For example,where the processing element comprises a general-purpose processorconfigured using software, the general-purpose processor may beconfigured as respective different processing elements at differenttimes. Software may accordingly configure the processing element toconstitute a particular hardware configuration at one instance of timeand to constitute a different hardware configuration at a differentinstance of time.

Computer hardware components, such as communication elements, memoryelements, processing elements, and the like, may provide information to,and receive information from, other computer hardware components.Accordingly, the described computer hardware components may be regardedas being communicatively coupled. Where multiple of such computerhardware components exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the computer hardware components. In embodimentsin which multiple computer hardware components are configured orinstantiated at different times, communications between such computerhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplecomputer hardware components have access. For example, one computerhardware component may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther computer hardware component may then, at a later time, accessthe memory device to retrieve and process the stored output. Computerhardware components may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processing elements thatare temporarily configured (e.g., by software) or permanently configuredto perform the relevant operations. Whether temporarily or permanentlyconfigured, such processing elements may constitute processingelement-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, comprise processing element-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processing element-implemented. For example, at least some ofthe operations of a method may be performed by one or more processingelements or processing element-implemented hardware modules. Theperformance of certain of the operations may be distributed among theone or more processing elements, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processing elements may be located in a single location(e.g., within a home environment, an office environment or as a serverfarm), while in other embodiments the processing elements may bedistributed across a number of locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer with a processing element andother computer hardware components) that manipulates or transforms datarepresented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s).

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat equivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

While the preferred embodiments have been described, it should beunderstood that the invention is not so limited and modifications may bemade without departing from the invention. The scope of the invention isdefined by the appended claims, and all devices that come within themeaning of the claims, either literally or by equivalence, are intendedto be embraced therein.

It is therefore intended that the foregoing detailed description beregarded as illustrative rather than limiting, and that it be understoodthat it is the following claims, including all equivalents, that areintended to define the spirit and scope of this invention.

1. A computer system configured to adjust a dynamically reconfigurableproduct of a customer, the system including a processor configured to:receive, at or via at least one transceiver over a wirelesscommunication channel, customer-related data including at least one of(i) an image, (ii) a picture, (iii) a video, or (iv) an audio recording,the customer-related data including time-stamped data units of GPSlocation collected from at least one of a mobile device or a smartvehicle controller; determine, based on the time-stamped data units ofGPS location indicative of residence, a new residence of a customer anda driving commute from the new residence to a place of employment orschool; perform at least one of object recognition, facial recognition,or optical character recognition on the at least one of (i) an image,(ii) a picture, (iii) a video, or (iv) an audio recording, and input theresult into a trained machine learning program to detect at least onecustomer activity, the customer activity including the driving commuteand driver driving behavior, driving characteristics, and/or drivingenvironment associated with the driving commute; receive current productdata derived from the dynamically reconfigurable product, the currentproduct data being stored in a memory unit; generate a customer profilefrom computer analysis of the customer-related data and the currentproduct data, the customer profile including at least one type of riskbased upon the customer activity; generate an updated dynamicallyreconfigurable product based, at least in part, upon the customerprofile; generate an electronic notification of the updated dynamicallyreconfigurable product; transmit, at or via the at least one transceiverover the wireless communication channel, the electronic notification toa computing device of the customer; and receive, at or via the at leastone transceiver over the wireless communication channel, a response,including approval, rej ection, or suggested changes, to the updateddynamically reconfigurable product from the computing device. 2.(canceled)
 3. The computer system of claim 1, wherein the customerprofile also includes an overall level of risk; and the customer-relateddata and the current product data indicate one or more features of ahome, and the overall level of risk is generated, at least in part,based upon the one or more features of the home, the dynamicallyreconfigurable product including homeowners insurance.
 4. The computersystem of claim 3, wherein the customer-related data and the currentproduct data indicate one or more features of a vehicle, and the overalllevel of risk is generated, at least in part, based upon the one or morefeatures of the vehicle, the dynamically reconfigurable productincluding auto insurance.
 5. The computer system of claim 3, wherein thecustomer-related data and the current product data indicate a number ofresidents in a residence, and the overall level of risk is generated, atleast in part, based upon the number of residents, the dynamicallyreconfigurable product including at least one of the homeownersinsurance and renters insurance.
 6. The computer system of claim 1,wherein the customer profile includes at least one pattern of activityassociated with at least one individual, and wherein the at least onerisk type is based, at least in part, on the at least one pattern ofactivity.
 7. The computer system of claim 3, wherein thecustomer-related data and current product data include personal and/orhealth-related information relevant to an underwriting process, and theoverall level of risk is generated, at least in part, based upon thecomputer analysis of the personal and/or health-related information, thedynamically reconfigurable product including at least one of healthinsurance and life insurance.
 8. The computer system of claim 7, whereinthe personal and/or health related information is comprised in at leastone of: (i) the image, (ii) the picture, (iii) the video, and (iv) theaudio recording.
 9. The computer system of claim 1, wherein the updateddynamically reconfigurable product includes at least one of (i) a newtype of insurance to add to the dynamically reconfigurable product, and(ii) a coverage amount for the new type of insurance, and the processoris further configured to adjust a premium for the dynamicallyreconfigurable product based upon the new type of insurance and thecoverage amount for the new type of insurance.
 10. The computer systemof claim 1, wherein the customer-related data includes sensor datacollected from at least one of (i) the mobile device, (ii) a smart homecontroller, and (iii) the smart vehicle controller.
 11. The computersystem of claim 10, wherein at least some of the sensor data iscollected from the smart home controller and the dynamicallyreconfigurable product includes homeowners insurance or rentersinsurance.
 12. The computer system of claim 11, wherein the customeractivity indicates that the customer is on a trip or vacation.
 13. Thecomputer system of claim 12, wherein the updated dynamicallyreconfigurable product includes an increased auto insurance premium. 14.The computer system of claim 13, wherein the updated dynamicallyreconfigurable product includes additional travel insurance.
 15. Thecomputer system of claim 14, wherein the processor is further configuredto: prior to generating the updated dynamically reconfigurable product,transmit, at or via the at least one transceiver over the wirelesscommunication channel, a prompt to the customer inquiring about a lengthof the trip or vacation; and receive, at or via the at least onetransceiver over the wireless communication channel, a response from thecomputing device indicating the length of the trip or vacation.
 16. Thecomputer system of claim 1, wherein the processor is further configuredto, prior to determining the customer activity, train the trainedmachine learning program by inputting a sample data set and executingthe trained machine learning program, the sample data set including atleast one of: (i) an image, (ii) a picture, (iii) a video, and (iv) anaudiorecording.
 17. The computer system of claim 16, wherein theprocessor is further configured to, prior to inputting the sample dataset, apply at least one of an object recognition program, facialrecognition program and an optical character recognition program to thesample data set.
 18. The computer system of claim 17, wherein theupdated dynamically reconfigurable product includes an increased healthinsurance premium.
 19. The computer system of claim 1, wherein thecustomer-related data indicates that the customer's finances haveundergone a significant change, and the updated dynamicallyreconfigurable product includes an additional financial product.
 20. Thecomputer system of claim 19, wherein the significant change results frommarriage or a child, and the additional financial product is a newsavings account.